Mercurial > repos > iuc > mageck_test
changeset 4:38073c236d71 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mageck commit 4478aabdcb10e4787450b1b23944defa7dc38ffe
| author | iuc |
|---|---|
| date | Mon, 04 Jun 2018 10:58:16 -0400 |
| parents | afa81ef5f1c3 |
| children | 688a8eccea71 |
| files | mageck_macros.xml mageck_test.xml test-data/in.mle.sgrnaeff test-data/out.count.bam.txt test-data/out.count.fastq.txt test-data/out.count.txt test-data/out.count_multi.txt test-data/out.test.R test-data/out.test.log.txt test-data/out.test.report.pdf test-data/out.test.sgrna_summary.txt test-data/output.count_normalized.txt test-data/output_summary.Rnw |
| diffstat | 13 files changed, 146 insertions(+), 3117 deletions(-) [+] |
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--- a/mageck_macros.xml Thu Apr 19 05:35:12 2018 -0400 +++ b/mageck_macros.xml Mon Jun 04 10:58:16 2018 -0400 @@ -6,7 +6,6 @@ <xml name="requirements"> <requirements> <requirement type="package" version="@VERSION@">mageck</requirement> - <requirement type="package" version="1.14.2">numpy</requirement> <requirement type="package" version="3.0.1">r-gplots</requirement> <requirement type="package" version="1.8_2">r-xtable</requirement> <yield/> @@ -15,7 +14,7 @@ <xml name="version"> <version_command><![CDATA[ - echo $(mageck -v )", numpy version" $([python -c "import numpy; numpy.version.version"])", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ") + echo $(mageck -v )", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ") ]]></version_command> </xml>
--- a/mageck_test.xml Thu Apr 19 05:35:12 2018 -0400 +++ b/mageck_test.xml Mon Jun 04 10:58:16 2018 -0400 @@ -160,8 +160,18 @@ <param name="count_table" value="in.test.sample.txt" ftype="tabular" /> <param name="treatment_id" value="HL60_final,KBM7_final" /> <param name="control_id" value="HL60_initial,KBM7_initial" /> - <output name="gene_summary" file="out.test.gene_summary.txt"/> - <output name="sgrna_summary" file="out.test.sgrna_summary.txt"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="id\tnum\tneg|score\tneg|p-value\tneg|fdr\tneg|rank\tneg|goodsgrna\tneg|lfc\tpos|score\tpos|p-value\tpos|fdr\tpos|rank\tpos|goodsgrna\tpos|lfc" /> + <has_text_matching expression="ACD.*10.*0.9996.*-0.0296" /> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="sgrna\tGene\tcontrol_count\ttreatment_count\tcontrol_mean\ttreat_mean\tLFC\tcontrol_var\tadj_var\tscore\tp.low\tp.high\tp.twosided\tFDR\thigh_in_treatment" /> + <has_text_matching expression="ADCK3_m227149144.*ADCK3.*263.11.*189.27.*False" /> + </assert_contents> + </output> </test> <!-- Ensure MAGeCK's additional outputs works --> <test expect_num_outputs="7"> @@ -172,13 +182,35 @@ <param name="normcounts" value="True" /> <param name="pdfreport" value="True" /> <param name="rfilesOpt" value="True" /> - <output name="gene_summary" file="out.test.gene_summary.txt"/> - <output name="sgrna_summary" file="out.test.sgrna_summary.txt"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="id\tnum\tneg|score\tneg|p-value\tneg|fdr\tneg|rank\tneg|goodsgrna\tneg|lfc\tpos|score\tpos|p-value\tpos|fdr\tpos|rank\tpos|goodsgrna\tpos|lfc" /> + <has_text_matching expression="ACD.*10.*0.9996.*-0.0296" /> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="sgrna\tGene\tcontrol_count\ttreatment_count\tcontrol_mean\ttreat_mean\tLFC\tcontrol_var\tadj_var\tscore\tp.low\tp.high\tp.twosided\tFDR\thigh_in_treatment" /> + <has_text_matching expression="ADCK3_m227149144.*ADCK3.*263.11.*189.27.*False" /> + </assert_contents> + </output> + <output name="log"> + <assert_contents> + <has_text_matching expression="Welcome to MAGeCK" /> + </assert_contents> + </output> + <output name="rscript"> + <assert_contents> + <has_text_matching expression="output_summary.tex" /> + </assert_contents> + </output> + <output name="rnwfile"> + <assert_contents> + <has_text_matching expression="This is a template file for Sweave used in MAGeCK" /> + </assert_contents> + </output> + <output name="plots" file="out.test.plots.pdf" compare="sim_size"/> <output name="normcounts" file="out.test.normalized.txt"/> - <output name="log" file="out.test.log.txt" compare="sim_size"/> - <output name="plots" file="out.test.plots.pdf" compare="sim_size"/> - <output name="rscript" file="out.test.R" /> - <output name="rnwfile" file="output_summary.Rnw" /> </test> </tests>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in.mle.sgrnaeff Mon Jun 04 10:58:16 2018 -0400 @@ -0,0 +1,100 @@ +CATGGCCATGGGCACCCGCC INO80B_m74682554 INO80B -0.005239 +AGGAGCTGCACCGCCACGCC NHS_p17705966 NHS -0.018779 +AACCACCAGCTGGTCCCGCC MED14_m40594623 MED14 -0.018132 +CAGCAACAGCCACCGCCATT MX2_p42748920 MX2 -0.012714 +CAGGCCTCACCTGCACCGCC SH3GLB2_p131790360 SH3GLB2 -0.072219 +ATGATTCTTCACCTGCCGCT RECQL_m21644465 RECQL -0.106925 +CTGCACCAGCCATATCGCGC CCDC170_p151815274 CCDC170 -0.015925 +ACGTCTCCGCCAGCCACTCC MEIS2_p37388538 MEIS2 -0.061877 +AAGGTGGCATGGACCCGCCA NTRK2_p87285677 NTRK2 0.062009 +GCTTCAAAGCACCCGCCGCC AIFM1_p129299590 AIFM1 -0.116716 +AGGACGAGGGCCGCCGTGAC PIN1_m9949250 PIN1 0.084799 +GGATTTCAGAACCGCCTGTG SNTG1_p51306800 SNTG1 -0.002402 +CGCTGCCGTTCCCGCCGCTC PHLPP1_m60383095 PHLPP1 -0.065516 +TCTTCATCACGCCGCCCGCC DIS3_p73355915 DIS3 -0.178900 +TAATCCTTGCCCGCCATGTC APLF_p68694848 APLF -0.125824 +CGAGGAAGGCGAGAACCGCC HNRPLL_m38829641 HNRPLL 0.208905 +TAATGATAAGCAGACCCGCC SCRN1_m29994913 SCRN1 -0.042915 +GGAGCCGCCGCCGCCATATC RNF11_p51702529 RNF11 -0.022538 +AAGAGCGCACCTGCCACTGG HIST3H3_m228612925 HIST3H3 0.041563 +ACAGATGTCCAGCAACCGCC SETD1B_p122243019 SETD1B -0.022566 +GAACAAAGAACCGCCGGCGC TMEM165_p56262481 TMEM165 -0.068287 +GGAACAGAAGCTGTCCCGCC MAP2K7_p7968846 MAP2K7 -0.001348 +ACGTTCCCGCCGCCGCCGTT TAF5_m105127878 TAF5 -0.075037 +TTATGCCCTGAACACCCGCC FAM184B_m17711242 FAM184B -0.060696 +TCTCCTCCACCCGCCGGGTC ZW10_p113644288 ZW10 -0.132037 +AAAGTTGCAGCCGCCACTGC SNAPC3_m15422944 SNAPC3 -0.064822 +GAGCTGAGCCTGCCACGCCG MAST3_p18218399 MAST3 0.097420 +TAGTCTTGTCCCTACCCGCC DDX31_m135545488 DDX31 -0.182423 +GACGGCGTGCAGCTCCCGCC EIF4EBP1_p37888163 EIF4EBP1 0.027745 +CAGGACTACCGCCATATCCC ZNF35_m44694064 ZNF35 -0.065333 +CGGCCGCTACCGCCGCTACC DSTYK_m205180551 DSTYK -0.121018 +GACCTTTGGAGTTCATCAAA TCF12_p57524508 TCF12 0.075702 +TGGGAAGGCGTCCGACCGCC C21orf33_m45553648 C21orf33 -0.011841 +CCGGGACCGCCACTGCCGAG SYN1_m47478971 SYN1 0.168301 +CTCGGACGAGCGGCTGGGCC TXNRD3_p126373683 TXNRD3 -0.003583 +TCTCATTGACCGGACCCGCC SNRNP200_m96970554 SNRNP200 -0.101923 +GGAGCGCACCCGCCGCGGAA C9orf69_p139008710 C9orf69 0.127616 +TCGCTTCCGCCGCCACTGCC NPAS1_m47524322 NPAS1 -0.003985 +GGATGCGACCGCCACTATCG SMARCA1_m128657283 SMARCA1 0.063867 +CCCAAATGCGCCCGCCTGCA WDR77_m111991712 WDR77 0.023250 +CTCACGAGCCGAGCCTCTCG PNCK_p152938458 PNCK 0.124364 +CAACGAAGACGCTCCCGCCT SENP3_m7466474 SENP3 0.024559 +GCCGCCGAAACCGCCACGGA RFWD2_p176175980 RFWD2 0.003107 +GCTCACCTTCACCGCCGCTC C9orf9_p135759399 C9orf9 -0.134392 +TGAAACCCTGGCCCGCCAGT C4orf22_p81283909 C4orf22 -0.016684 +GGGCTGCTCCTGCTTCCTCC ZNF827_p146859532 ZNF827 -0.094275 +GTGGTGCCACCCGCCGTGGC EPM2A_m146056593 EPM2A -0.112144 +GTGTATCCAGTGCCTGCTCT TMEM175_m941547 TMEM175 -0.249848 +GCAGCGCCAGTCCCGCCAGC DUSP23_m159750934 DUSP23 -0.134388 +AGTGGCTGCTCCCGCCATAC YLPM1_m75230212 YLPM1 -0.007259 +AGCACCACCAGCTGTTGCTC RAB1A_p65315689 RAB1A -0.032893 +AGTGGTTCCCGCCGCAGGAC GSG2_p3627326 GSG2 0.033727 +CAGAACCCGCCACTTGTCCA RPL10L_p47120303 RPL10L 0.026564 +GACCATGAACCGCAGCCGCC PWP1_p108079672 PWP1 -0.090761 +AAGGAGGATAGAGGCCCGCC KIAA1755_p36874470 KIAA1755 0.005710 +TCTCCAATACCTGCCGATTC JAZF1_m28220148 JAZF1 -0.153656 +CGTTCACCCGCCGGGCCTTT PAXBP1_p34143951 PAXBP1 -0.074149 +GGTAGCCAGGGCACCCGCCA BMPR2_m203242221 BMPR2 0.152337 +GCCTCCACCGCCCGCCGCTT UXT_p47518291 UXT -0.042766 +ACTCTCACCGCCGTAGGTGC POU2AF1_p111229504 POU2AF1 -0.026811 +CTGGACGACCGCCACGACAG NFKBIA_m35873755 NFKBIA 0.076979 +CTGAATCCCGCCACACTCTC LPIN1_p11905730 LPIN1 -0.025123 +TACCGGAGCCGCGACCGCCT ZNF746_p149194600 ZNF746 0.173225 +TTTATCTGCATTTCCATGAC SYNPO2_p119810206 SYNPO2 -0.140214 +TCACTCCTGAACAGACTTCT LTA4H_m96421257 LTA4H -0.030193 +GATGGTCGCCGCCTGCCGCT TMEM87B_p112813167 TMEM87B 0.036418 +CTGGATGGAGCCGCCGCTCC OCRL_p128674412 OCRL -0.150457 +AGTGGACATCCGCCATAACA RPL18_m49121112 RPL18 0.056941 +GACAAGGCGAAACCGCCGCC PSMD3_p38137259 PSMD3 0.030238 +AGCCACACCGAGAACCGCCG WBP2NL_p42394837 WBP2NL 0.131323 +CGGCCTCCAGCCGCCACTTG LUZP1_m23420706 LUZP1 -0.002771 +ATTCTCTTTGGAGCCGTGAG STRADB_p202340406 STRADB -0.001874 +GCCTCTTCTCCGCGCTCTCG FOXL2_p138665341 FOXL2 -0.150694 +CTGCACCCAACCGCCGGCAC ADRA1A_m26722428 ADRA1A -0.096324 +TCACCCAGCCATACCAGCCG RBBP9_p18477729 RBBP9 0.064510 +AGTCCTCCCGCCGCTGCAGC IMP3_p75932376 IMP3 -0.091312 +TGGTGTCTCATCTCCTTGCC RABGAP1_m125719426 RABGAP1 -0.201102 +TTTAGGAGCTTCTCCAAATT RARS_p167915662 RARS -0.079412 +ACAGGCCCGCCACGTCCGTC RPRM_p154335036 RPRM 0.003556 +GGACATGAAGGAGTCCCGCC OBFC1_m105677228 OBFC1 0.039483 +GAGCTGCGGGACCCGCCACC TINF2_p24711504 TINF2 -0.010376 +TTCTTCCAGAGAGAACTCTA ZNF565_p36686010 ZNF565 0.131393 +CACATTCTCCACCCGCCGAG CCDC78_p776304 CCDC78 0.076491 +TAGGCGCCCGCCGCTCTTCC YWHAB_p43530334 YWHAB -0.008675 +CGCTCCCGCCGCTGCTTCCT CRX_m48339507 CRX -0.050130 +AGCGGCACCTACACCCGCCA EXOSC1_m99205546 EXOSC1 0.090355 +CGCGGTGGGCAAGACGAGCC RHOU_p228871662 RHOU 0.144042 +TTCTCAAGAAATTCACCGCC CHMP1B_m11851692 CHMP1B -0.112913 +CACCGCCACCGCCACGACCA U2AF1_p44513288 U2AF1 0.080210 +TCCCAACACCCGCCAAGAGA NET1_p5494387 NET1 0.089082 +TCTGAGCTCCAGGTGCTTCT PIAS3_p145578085 PIAS3 -0.027557 +TTCGCCCGCCGGCTCCTGCG CMPK2_m7005801 CMPK2 0.105524 +ACCAGCCAAGATTGCCCGCC SATB1_m18462362 SATB1 -0.070132 +GACACACCTCGCCCGCCTCC FOXA3_m46367770 FOXA3 -0.054864 +AAATTCCCAGGAGAAATATA ZNF627_p11725680 ZNF627 0.090453 +GTCACGGCCGCCCGCCGACA MLLT4_m168227813 MLLT4 0.040685 +AACTGCCTGCACCGCCTCTA FAM120A_p96214350 FAM120A 0.010379 +TTATGAAAGTATTTCTCTCC ADNP2_m77891006 ADNP2 -0.159761 +CGCACCCTCACCGCCGGCCT CD3EAP_m45909967 CD3EAP -0.028605 +GTGGACCCTCGTGAGCGACC HSF1_p145515504 HSF1 -0.051256
--- a/test-data/out.count.bam.txt Thu Apr 19 05:35:12 2018 -0400 +++ b/test-data/out.count.bam.txt Mon Jun 04 10:58:16 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_bam +sgRNA Gene test1.bam s_10007 CCNA1 0 s_10008 CCNA1 0 s_10027 CCNC 0
--- a/test-data/out.count.fastq.txt Thu Apr 19 05:35:12 2018 -0400 +++ b/test-data/out.count.fastq.txt Mon Jun 04 10:58:16 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 1 s_24835 HCFC1R1 1 s_14784 CYP4B1 4
--- a/test-data/out.count.txt Thu Apr 19 05:35:12 2018 -0400 +++ b/test-data/out.count.txt Mon Jun 04 10:58:16 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 1 s_24835 HCFC1R1 1 s_14784 CYP4B1 4
--- a/test-data/out.count_multi.txt Thu Apr 19 05:35:12 2018 -0400 +++ b/test-data/out.count_multi.txt Mon Jun 04 10:58:16 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz test2_fastq_gz +sgRNA Gene test1.fastq.gz test2.fastq.gz s_47512 RNF111 1 0 s_24835 HCFC1R1 1 0 s_14784 CYP4B1 4 0
--- a/test-data/out.test.R Thu Apr 19 05:35:12 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,930 +0,0 @@ -pdf(file='output.pdf',width=4.5,height=4.5); -gstable=read.table('output.gene_summary.txt',header=T) -# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=3 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1") -# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285)) -targetgene="ACIN1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424)) -targetgene="ACTR8" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344)) -targetgene="AHCY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021)) -targetgene="ACLY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173)) -targetgene="AATF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033)) -targetgene="AGBL5" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991)) -targetgene="AHCTF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427)) -targetgene="ABT1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338)) -targetgene="ADIRF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927)) -targetgene="ABCF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=9 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP") -# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192)) -targetgene="ACRC" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882)) -targetgene="AGAP3" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471)) -targetgene="ADCK4" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885)) -targetgene="AHRR" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979)) -targetgene="ADRBK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076)) -targetgene="ADK" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816)) -targetgene="ADCK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512)) -targetgene="ADARB2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832)) -targetgene="ACSS2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625)) -targetgene="ADNP" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -dev.off() -Sweave("output_summary.Rnw"); -library(tools); - -texi2dvi("output_summary.tex",pdf=TRUE); -
--- a/test-data/out.test.log.txt Thu Apr 19 05:35:12 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,109 +0,0 @@ -INFO @ Mon, 26 Mar 2018 08:37:53: Parameters: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/bin/mageck test -k /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat -t HL60_final,KBM7_final -c HL60_initial,KBM7_initial -n output --normcounts-to-file --pdf-report --norm-method median --adjust-method fdr --sort-criteria neg --remove-zero both --gene-lfc-method median -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to MAGeCK v0.5.7. Command: test -INFO @ Mon, 26 Mar 2018 08:37:53: Loading count table from /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat -INFO @ Mon, 26 Mar 2018 08:37:53: Processing 1 lines.. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Parsing error in line 1 (usually the header line). Skip this line. -INFO @ Mon, 26 Mar 2018 08:37:53: Loaded 999 records. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.RTemplate. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template_indvgene.RTemplate. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading Rnw template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.Rnw. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Setting up the visualization module... -DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_final,KBM7_final -DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 2 3 -INFO @ Mon, 26 Mar 2018 08:37:53: Treatment samples:HL60_final,KBM7_final -INFO @ Mon, 26 Mar 2018 08:37:53: Treatment sample index:2,3 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_initial,KBM7_initial -DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 0 1 -INFO @ Mon, 26 Mar 2018 08:37:53: Control samples:HL60_initial,KBM7_initial -INFO @ Mon, 26 Mar 2018 08:37:53: Control sample index:0,1 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Initial (total) size factor: 1.6666455325878438 2.027372749328462 0.7198064117880387 0.6589869375844738 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Median factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 -INFO @ Mon, 26 Mar 2018 08:37:53: Final size factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 -INFO @ Mon, 26 Mar 2018 08:37:53: Writing normalized read counts to output.normalized.txt -DEBUG @ Mon, 26 Mar 2018 08:37:53: Adjusted model: 1.1175084644498339 3.4299551007579927 -INFO @ Mon, 26 Mar 2018 08:37:53: Raw variance calculation: 0.5 for control, 0.5 for treatment -INFO @ Mon, 26 Mar 2018 08:37:53: Adjusted variance calculation: 0.3333333333333333 for raw variance, 0.6666666666666667 for modeling -INFO @ Mon, 26 Mar 2018 08:37:53: Use qnorm to reversely calculate sgRNA scores ... -DEBUG @ Mon, 26 Mar 2018 08:37:53: lower test FDR cutoff: 0.3283283283283283 -DEBUG @ Mon, 26 Mar 2018 08:37:53: higher test FDR cutoff: 0.34534534534534533 -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.plow.txt -o output.gene.low.txt -p 0.3283283283283283 --skip-gene NA --skip-gene na -INFO @ Mon, 26 Mar 2018 08:37:53: Command message: -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to RRA v 0.5.7. -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene NA for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene na for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Reading input file... -INFO @ Mon, 26 Mar 2018 08:37:53: Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 -INFO @ Mon, 26 Mar 2018 08:37:53: Computing lo-values for each group... -INFO @ Mon, 26 Mar 2018 08:37:53: Computing false discovery rate... -INFO @ Mon, 26 Mar 2018 08:37:53: Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 9 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 10 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Number of genes under FDR adjustment: 100 -INFO @ Mon, 26 Mar 2018 08:37:53: Saving to output file... -INFO @ Mon, 26 Mar 2018 08:37:53: RRA completed. -INFO @ Mon, 26 Mar 2018 08:37:53: -INFO @ Mon, 26 Mar 2018 08:37:53: End command message. -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.phigh.txt -o output.gene.high.txt -p 0.34534534534534533 --skip-gene NA --skip-gene na -INFO @ Mon, 26 Mar 2018 08:37:53: Command message: -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to RRA v 0.5.7. -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene NA for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene na for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Reading input file... -INFO @ Mon, 26 Mar 2018 08:37:53: Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 -INFO @ Mon, 26 Mar 2018 08:37:53: Computing lo-values for each group... -INFO @ Mon, 26 Mar 2018 08:37:53: Computing false discovery rate... -INFO @ Mon, 26 Mar 2018 08:37:53: Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 9 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 10 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Number of genes under FDR adjustment: 100 -INFO @ Mon, 26 Mar 2018 08:37:53: Saving to output file... -INFO @ Mon, 26 Mar 2018 08:37:53: RRA completed. -INFO @ Mon, 26 Mar 2018 08:37:53: -INFO @ Mon, 26 Mar 2018 08:37:53: End command message. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Sorting the merged items by negative selection... -INFO @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.low.txt: ACIN1,ACTR8,AHCY,ACLY,AATF,AGBL5,AHCTF1,ABT1,ADIRF,ABCF1 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:3 -INFO @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.high.txt: ACRC,AGAP3,ADCK4,AHRR,ADRBK1,ADK,ADCK1,ADARB2,ACSS2,ADNP -DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:9 -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.plow.txt -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.phigh.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.low.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.high.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: cd ./; Rscript output.R -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: null device -INFO @ Mon, 26 Mar 2018 08:37:59: 1 -INFO @ Mon, 26 Mar 2018 08:37:59: Writing to file output_summary.tex -INFO @ Mon, 26 Mar 2018 08:37:59: Processing code chunks with options ... -INFO @ Mon, 26 Mar 2018 08:37:59: 1 : keep.source term verbatim (label = funcdef, output_summary.Rnw:27) -INFO @ Mon, 26 Mar 2018 08:37:59: 2 : keep.source term tex (label = tab1, output_summary.Rnw:37) -INFO @ Mon, 26 Mar 2018 08:37:59: 3 : keep.source term verbatim (output_summary.Rnw:77) -INFO @ Mon, 26 Mar 2018 08:37:59: 4 : keep.source term verbatim pdf (output_summary.Rnw:83) -INFO @ Mon, 26 Mar 2018 08:37:59: 5 : keep.source term verbatim pdf (output_summary.Rnw:201) -INFO @ Mon, 26 Mar 2018 08:37:59: 6 : keep.source term verbatim pdf (output_summary.Rnw:345) -INFO @ Mon, 26 Mar 2018 08:37:59: 7 : keep.source term verbatim pdf (output_summary.Rnw:489) -INFO @ Mon, 26 Mar 2018 08:37:59: 8 : keep.source term verbatim (output_summary.Rnw:567) -INFO @ Mon, 26 Mar 2018 08:37:59: 9 : keep.source term verbatim pdf (output_summary.Rnw:573) -INFO @ Mon, 26 Mar 2018 08:37:59: 10 : keep.source term verbatim pdf (output_summary.Rnw:691) -INFO @ Mon, 26 Mar 2018 08:37:59: 11 : keep.source term verbatim pdf (output_summary.Rnw:835) -INFO @ Mon, 26 Mar 2018 08:37:59: 12 : keep.source term verbatim pdf (output_summary.Rnw:979) -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: You can now run (pdf)latex on ‘output_summary.tex’ -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary-*.pdf -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.aux -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.tex -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.toc -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message.
--- a/test-data/out.test.sgrna_summary.txt Thu Apr 19 05:35:12 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1000 +0,0 @@ -sgrna Gene control_count treatment_count control_mean treat_mean LFC control_var adj_var score p.low p.high p.twosided FDR high_in_treatment -AHRR_p344008 AHRR 251.35/221.99 1564.6/1502.5 236.67 1533.6 2.6908 1178.2 5085.6 18.186 1 3.3319e-74 6.6638e-74 6.6571e-71 True -ACAD9_m128598565 ACAD9 739.35/925.27 2528/2050.7 832.31 2289.3 1.4586 65590 35597 7.7226 1 5.7002e-15 1.14e-14 5.6945e-12 True -ACTR8_m53916081 ACTR8 1925.5/2054.8 194.94/235.17 1990.2 215.06 -3.2041 4580.2 54357 7.6137 1.3311e-14 1 2.6622e-14 7.8608e-12 False -ACRC_m70814198 ACRC 76.433/90.573 447.3/357.04 83.503 402.17 2.2543 2086.7 1760.4 7.5952 1 1.5737e-14 3.1475e-14 7.8608e-12 True -ABCC1_p16101710 ABCC1 52.915/26.639 203.78/224.3 39.777 214.04 2.3987 277.85 700.66 6.5833 1 2.465e-11 4.93e-11 9.8502e-09 True -ACTR8_m53916067 ACTR8 1267/1156.1 251.09/42.341 1211.6 146.72 -3.0372 13968 31286 6.021 8.6646e-10 1 1.7329e-09 2.3419e-07 False -AAK1_m69870125 AAK1 402.74/621.58 1149.5/1202.2 512.16 1175.8 1.1974 12666 12223 6.0027 1 9.7009e-10 1.9402e-09 2.3419e-07 True -ADCK1_p78285331 ADCK1 798.14/768.99 1478.8/1756 783.56 1617.4 1.0446 19425 19317 5.9996 1 9.8882e-10 1.9776e-09 2.3419e-07 True -AHCY_m32883247 AHCY 1142.1/1099.3 112.93/100.7 1120.7 106.82 -3.379 494.86 28685 5.9891 1.0549e-09 1 2.1098e-09 2.3419e-07 False -AHCTF1_m247070995 AHCTF1 1437.5/1095.8 320.49/161.36 1266.6 240.92 -2.3895 35534 33759 5.5826 1.1847e-08 1 2.3695e-08 2.3671e-06 False -AHCY_p32883309 AHCY 1053.9/882.65 106.62/105.85 968.27 106.24 -3.1761 7331.9 24378 5.524 1.6565e-08 1 3.3129e-08 3.0087e-06 False -ADCY1_p45614315 ADCY1 2.9397/72.814 210.72/187.1 37.877 198.91 2.3624 1360 895.72 5.3806 1 4.1374e-08 8.2747e-08 6.6138e-06 True -ACTN4_p39138476 ACTN4 449.78/475.95 1056.7/977.86 462.87 1017.3 1.1344 1726.9 10724 5.3539 1 4.3033e-08 8.6065e-08 6.6138e-06 True -ADAM12_m128076658 ADAM12 768.74/767.21 1432.1/1562.6 767.98 1497.4 0.96239 4258.6 18836 5.3145 1 5.3464e-08 1.0693e-07 7.63e-06 True -ABT1_m26597388 ABT1 1743.3/1980.2 837.19/281.51 1861.7 559.35 -1.733 91226 64053 5.1459 1.3309e-07 1 2.6618e-07 1.7727e-05 False -ACRC_p70814182 ACRC 682.02/822.26 1572.2/1333.8 752.14 1453 0.949 19128 18646 5.1324 1 1.4307e-07 2.8614e-07 1.7866e-05 True -ACBD6_p180471256 ACBD6 107.3/285.93 553.29/687.76 196.61 620.53 1.6531 12498 6924.5 5.0942 1 1.7666e-07 3.5333e-07 2.0382e-05 True -ACRC_p70811990 ACRC 495.35/605.6 1159.6/1617.6 550.47 1388.6 1.3333 55476 27162 5.0853 1 1.8362e-07 3.6724e-07 2.0382e-05 True -ADRA1B_m159344001 ADRA1B 329.25/309.01 881.98/676.32 319.13 779.15 1.2851 10677 8286.5 5.0535 1 2.1699e-07 4.3398e-07 2.2818e-05 True -AHCY_p32883238 AHCY 61.735/218.44 614.49/452.6 140.09 533.54 1.9217 12691 6123 5.0282 1 2.5699e-07 5.1398e-07 2.5673e-05 True -ACTR5_m37377141 ACTR5 865.75/935.92 1595.5/1695.4 900.84 1645.4 0.86841 3723.6 22496 4.9645 1 3.4447e-07 6.8893e-07 3.2774e-05 True -ADARB2_m1779330 ADARB2 135.23/118.99 351.41/399.95 127.11 375.68 1.556 655.19 2547.9 4.9245 1 4.2536e-07 8.5071e-07 3.863e-05 True -ADRB1_m115804012 ADRB1 2166.6/1942.9 1093.3/705.5 2054.7 899.42 -1.191 50114 56324 4.8681 5.6346e-07 1 1.1269e-06 4.8948e-05 False -AHCTF1_m247070906 AHCTF1 1078.9/1609 348.88/193.97 1343.9 271.43 -2.3036 76257 48827 4.8539 6.0526e-07 1 1.2105e-06 5.0388e-05 False -ACIN1_m23538803 ACIN1 2432.6/2122.3 1067.5/155.63 2277.4 611.55 -1.8952 2.3194e+05 1.1942e+05 4.8207 7.1537e-07 1 1.4307e-06 5.7173e-05 False -ACIN1_m23538698 ACIN1 3424.8/3818.3 1992.3/690.05 3621.5 1341.2 -1.4324 4.627e+05 2.2481e+05 4.8094 7.5677e-07 1 1.5135e-06 5.8155e-05 False -ABCF1_p30545878 ABCF1 2012.3/1989.1 1064.3/432 2000.7 748.15 -1.4179 1.0009e+05 69814 4.7403 1.0669e-06 1 2.1338e-06 7.8951e-05 False -ABCF1_p30539251 ABCF1 1127.4/1198.8 371.59/370.2 1163.1 370.9 -1.6462 1274 29895 4.5817 2.3062e-06 1 4.6124e-06 0.00016456 False -ACTR1A_m104250365 ACTR1A 402.74/161.61 711.64/716.37 282.18 714.01 1.3362 14542 8970.1 4.5595 1 2.5679e-06 5.1359e-06 0.00017692 True -ADRBK1_m67034181 ADRBK1 371.88/877.32 2395.5/1564.9 624.6 1980.2 1.6631 2.3633e+05 88755 4.5503 1 2.728e-06 5.4561e-06 0.00018042 True -AGL_m100327135 AGL 221.95/118.99 418.91/461.75 170.47 440.33 1.3639 3109.1 3531 4.5414 1 2.7993e-06 5.5987e-06 0.00018042 True -AHCY_p32883253 AHCY 651.15/879.09 237.21/18.882 765.12 128.05 -2.5697 24907 20808 4.4189 4.9594e-06 1 9.9187e-06 0.00030965 False -AGTPBP1_m88296213 AGTPBP1 780.5/873.77 1431.5/1476.2 827.13 1453.9 0.81294 2675 20458 4.3817 0.99999 5.8866e-06 1.1773e-05 0.00035641 True -ADK_p76153940 ADK 804.02/472.4 1336.2/1203.3 638.21 1269.8 0.99133 31910 20857 4.373 0.99999 6.1269e-06 1.2254e-05 0.00036004 True -ACD_m67694254 ACD 1174.4/934.15 1644.1/1830.4 1054.3 1737.3 0.72 23111 26800 4.1719 0.99998 1.5104e-05 3.0208e-05 0.00086223 True -ACTR1A_p104262383 ACTR1A 1240.6/836.47 215.76/358.76 1038.5 287.26 -1.8505 45936 32881 4.1431 1.7134e-05 0.99998 3.4269e-05 0.00094812 False -AAK1_p69870105 AAK1 279.28/289.48 581.68/639.7 284.38 610.69 1.0999 867.56 6237.8 4.1316 0.99998 1.8016e-05 3.6033e-05 0.00094812 True -ACTR8_m53916096 ACTR8 1719.7/1007 356.45/222.01 1363.4 289.23 -2.233 1.3154e+05 67630 4.1313 1.8032e-05 0.99998 3.6065e-05 0.00094812 False -ACSS2_p33500893 ACSS2 94.072/447.54 711.64/927.51 270.81 819.57 1.5941 42884 18233 4.0641 0.99998 2.4667e-05 4.9333e-05 0.0012637 True -ACHE_p100491821 ACHE 864.28/1113.5 382.32/331.29 988.9 356.81 -1.4681 16180 24957 4.0012 3.1512e-05 0.99997 6.3025e-05 0.001574 False -ACSL6_m131326651 ACSL6 630.57/692.62 1066.8/1423.6 661.6 1245.2 0.91134 32780 21565 3.9742 0.99996 3.5303e-05 7.0606e-05 0.0017204 True -ADCK4_m41220474 ADCK4 1978.4/1751.1 2659.2/2851.2 1864.8 2755.2 0.56291 22137 50558 3.9601 0.99996 3.7465e-05 7.4931e-05 0.0017823 True -ACLY_p40070033 ACLY 1594.8/1108.2 605.65/127.6 1351.5 366.63 -1.8793 1.1633e+05 62333 3.945 3.9906e-05 0.99996 7.9812e-05 0.0018542 False -ACTRT3_m169487204 ACTRT3 377.76/277.05 599.35/716.94 327.4 658.14 1.0051 5992.9 7296 3.8721 0.99995 5.3953e-05 0.00010791 0.00245 True -ADRB1_m115804005 ADRB1 1346.4/1584.1 791.77/633.98 1465.3 712.87 -1.0384 20355 38658 3.8267 6.4926e-05 0.99994 0.00012985 0.0028827 False -ABCF1_p30545181 ABCF1 1164.1/1204.1 1687/2089 1184.1 1888 0.67261 40807 33934 3.8212 0.99993 6.6413e-05 0.00013283 0.0028846 True -ABTB1_m127395813 ABTB1 492.41/353.41 863.06/731.82 422.91 797.44 0.91342 9135.6 9699.2 3.8029 0.99993 7.1501e-05 0.000143 0.0030341 True -ACLY_m40070008 ACLY 327.78/454.64 51.733/24.032 391.21 37.882 -3.3344 4215.3 8894 3.7981 7.2891e-05 0.99993 0.00014578 0.0030341 False -AGAP3_p150783818 AGAP3 388.05/509.7 933.09/750.13 448.87 841.61 0.90535 12068 10932 3.7563 0.99991 8.624e-05 0.00017248 0.003464 True -ABT1_p26597266 ABT1 986.28/745.9 328.06/302.11 866.09 315.09 -1.4559 14615 21533 3.755 8.6686e-05 0.99991 0.00017337 0.003464 False -ACTR3_p114691894 ACTR3 2171/2250.1 1406.3/1163.8 2210.6 1285 -0.78214 16259 61099 3.7443 9.0443e-05 0.99991 0.00018089 0.0035432 False -ADCK5_m145608343 ADCK5 936.31/790.3 1459.9/1358.4 863.3 1409.1 0.70621 7906.4 21456 3.7263 0.9999 9.7163e-05 0.00019433 0.0037333 True -ADRBK1_p67034199 ADRBK1 923.08/758.33 1479.4/1276 840.7 1377.7 0.71192 17136 20832 3.7206 0.9999 9.9387e-05 0.00019877 0.0037467 True -ACAT2_p160183972 ACAT2 138.17/277.05 441.62/673.46 207.61 557.54 1.4209 18259 9017 3.6851 0.99988 0.00011596 0.00023192 0.0042905 True -ADRA1A_m26722399 ADRA1A 161.69/230.87 376.64/490.36 196.28 433.5 1.1391 4429.7 4230 3.6474 0.99987 0.00013263 0.00026527 0.0047957 True -AGAP2_p58129171 AGAP2 508.58/907.51 1324.9/1248.5 708.04 1286.7 0.86084 41245 25220 3.6436 0.99987 0.00013441 0.00026883 0.0047957 True -ACVR1C_m158443841 ACVR1C 474.77/515.02 895.23/869.72 494.9 882.47 0.83315 567.92 11553 3.606 0.99984 0.0001555 0.00031101 0.0054509 True -AATF_m35306482 AATF 836.36/1289.3 468.75/177.95 1062.8 323.35 -1.7137 72440 42175 3.6011 0.00015844 0.99984 0.00031689 0.0054581 False -ADRBK1_p67034193 ADRBK1 908.38/832.92 1639.7/1268.5 870.65 1454.1 0.7393 35863 26394 3.5913 0.99984 0.00016449 0.00032897 0.0055702 True -ACIN1_m23538715 ACIN1 846.65/985.65 335/415.98 916.15 375.49 -1.2845 6469.8 22922 3.5711 0.00017778 0.99982 0.00035555 0.0059199 False -ADRA1A_m26722428 ADRA1A 0/0 68.136/184.24 4.4399 126.19 4.5473 3370.2 1164.4 3.568 0.99967 0.00032598 0.00065196 0.0095886 True -ADAP1_m994071 ADAP1 1148/719.26 262.45/345.03 933.61 303.74 -1.6168 47653 31490 3.5496 0.00019291 0.99981 0.00038582 0.0063186 False -ADRB3_m37823829 ADRB3 314.55/280.6 690.82/507.52 297.58 599.17 1.0073 8687.9 7269.7 3.5373 0.9998 0.00020218 0.00040435 0.0065153 True -ADNP2_p77891042 ADNP2 238.12/232.65 417.65/547.01 235.38 482.33 1.0319 4190.7 5054.9 3.4733 0.99974 0.00025717 0.00051434 0.008156 True -ACVR1_m158637033 ACVR1 958.36/756.55 1619.5/1261.1 857.45 1440.3 0.74755 42295 28294 3.465 0.99973 0.00026516 0.00053033 0.0082203 True -ADD1_p2877627 ADD1 236.65/310.79 502.82/804.49 273.72 653.65 1.2528 24125 12027 3.4644 0.99973 0.00026743 0.00053485 0.0082203 True -ABCB8_p150730712 ABCB8 249.88/381.83 630.26/574.47 315.85 602.36 0.92921 5130.8 7010.3 3.4219 0.99969 0.0003109 0.00062181 0.0094119 True -AHNAK2_m105423836 AHNAK2 293.97/816.94 1369/1079.1 555.46 1224.1 1.1385 89382 38551 3.4054 0.99967 0.00033114 0.00066227 0.0095886 True -ACVRL1_m52306276 ACVRL1 921.61/880.87 1418.9/1405.3 901.24 1412.1 0.64725 461.12 22507 3.405 0.99967 0.00033082 0.00066164 0.0095886 True -A1CF_m52596017 A1CF 432.14/731.69 980.4/1082 581.92 1031.2 0.82436 25013 17560 3.3905 0.99965 0.00034889 0.00069778 0.0099583 True -ACVR1C_p158443781 ACVR1C 492.41/459.97 729.31/981.29 476.19 855.3 0.84356 16137 12757 3.3565 0.99961 0.0003947 0.00078941 0.011107 True -AFF4_p132272844 AFF4 599.71/941.25 1211.3/1385.3 770.48 1298.3 0.75201 36727 24846 3.3485 0.99959 0.00040631 0.00081262 0.011275 True -ACHE_m100491767 ACHE 1556.6/1758.2 970.94/942.95 1657.4 956.95 -0.79177 10356 44340 3.3264 0.00043988 0.99956 0.00087977 0.01204 False -AATK_m79102281 AATK 683.49/816.94 1095.2/1516.9 750.21 1306 0.79901 48894 28533 3.2905 0.9995 0.00050001 0.001 0.0135 True -AFAP1L2_p116100418 AFAP1L2 388.05/362.29 687.67/664.87 375.17 676.27 0.84835 295.7 8489.3 3.268 0.99946 0.0005416 0.0010832 0.014428 True -ADCK5_m145608301 ADCK5 1051/703.28 1509.7/1321.2 877.12 1415.4 0.68979 39109 27595 3.2406 0.9994 0.00059631 0.0011926 0.01567 True -ADNP_p49510899 ADNP 1491.9/1332 2087.6/1983.2 1411.9 2035.4 0.52732 9123.3 37095 3.237 0.9994 0.00060389 0.0012078 0.01567 True -AFMID_m76187074 AFMID 898.09/880.87 506.6/310.69 889.48 408.65 -1.1202 9669.3 22181 3.2285 0.00062217 0.99938 0.0012443 0.015746 False -ACSS2_p33500928 ACSS2 399.8/806.28 1147.6/1059.1 603.04 1103.3 0.87047 43263 24017 3.2283 0.99938 0.00062259 0.0012452 0.015746 True -ACSS2_m33500924 ACSS2 595.3/591.39 1061.8/887.45 593.34 974.62 0.71502 7601.9 14136 3.2068 0.99933 0.0006711 0.0013422 0.016501 True -ACTR5_p37377211 ACTR5 1021.6/738.79 476.32/281.51 880.18 378.92 -1.2137 29477 24441 3.2063 0.00067225 0.99933 0.0013445 0.016501 False -ACVR1_m158637046 ACVR1 712.89/678.41 1296.5/986.44 695.65 1141.5 0.71364 24328 19358 3.2042 0.99932 0.00067723 0.0013545 0.016501 True -ACTL6B_p100253066 ACTL6B 983.34/1022.9 560.86/438.86 1003.1 499.86 -1.0035 4113 25357 3.1605 0.00078736 0.99921 0.0015747 0.018954 False -ACIN1_p23538735 ACIN1 246.94/177.59 28.39/0 212.27 14.195 -3.811 1403.6 4505.9 3.1549 0.00080283 0.9992 0.0016057 0.019092 False -ACTR1A_p104250290 ACTR1A 605.59/738.79 2045.3/1120.9 672.19 1583.1 1.2346 2.1808e+05 83522 3.152 0.99918 0.000819 0.001638 0.019092 True -ADH5_p100003219 ADH5 316.02/586.06 44.793/127.6 451.04 86.195 -2.3741 19945 13595 3.1481 0.00082178 0.99918 0.0016436 0.019092 False -ACTR8_m53916121 ACTR8 285.15/408.47 99.05/44.63 346.81 71.84 -2.2555 4541.9 7778.6 3.1316 0.00086926 0.99913 0.0017385 0.019963 False -ACTL6A_p179287878 ACTL6A 518.86/502.59 99.05/238.6 510.73 168.82 -1.5913 4934.7 11964 3.1263 0.00088515 0.99911 0.0017703 0.020097 False -ADRB3_p37823908 ADRB3 471.83/399.59 733.09/761.57 435.71 747.33 0.77701 1507.4 10026 3.1122 0.99907 0.00092859 0.0018572 0.020846 True -AGFG1_m228337276 AGFG1 2382.7/2260.8 1567.8/1499.1 2321.7 1533.4 -0.59811 4891.7 64529 3.1032 0.00095731 0.99904 0.0019146 0.021204 False -ACTL7A_m111624736 ACTL7A 873.1/829.37 364.65/436.57 851.24 400.61 -1.0854 1771.3 21122 3.1006 0.00096573 0.99903 0.0019315 0.021204 False -ADAM10_p58974401 ADAM10 968.64/864.89 590.51/256.34 916.77 423.43 -1.1126 30610 25496 3.0897 0.001002 0.999 0.0020039 0.02176 False -AFF3_m100625363 AFF3 1159.7/761.88 505.34/304.97 960.8 405.16 -1.2437 49608 32648 3.0752 0.0010519 0.99895 0.0021038 0.022599 False -ADCK3_p227149165 ADCK3 558.55/838.25 1063.7/1170.7 698.4 1117.2 0.67697 22420 18772 3.0566 0.99888 0.0011193 0.0022387 0.023792 True -ABT1_p26597300 ABT1 1562.5/763.66 422.07/220.29 1163.1 321.18 -1.8532 1.697e+05 76498 3.0473 0.0011547 0.99885 0.0023093 0.02425 False -AGBL5_m27275887 AGBL5 640.86/602.05 307.24/192.83 621.45 250.03 -1.3101 3649.6 14883 3.0445 0.0011652 0.99883 0.0023304 0.02425 False -ADARB1_m46595721 ADARB1 229.3/735.24 1186.1/914.35 482.27 1050.2 1.1211 82453 34968 3.0372 0.9988 0.0012001 0.0024001 0.024531 True -ABHD14B_m52004122 ABHD14B 1793.2/1836.3 983.56/1301.7 1814.8 1142.6 -0.66697 25770 49052 3.0349 0.0012032 0.9988 0.0024065 0.024531 False -ADK_p76154015 ADK 454.19/490.16 896.49/685.47 472.18 790.98 0.74309 11456 11128 3.0222 0.99875 0.0012548 0.0025096 0.025324 True -AGPAT5_m6566233 AGPAT5 3338.1/3873.3 2840.3/2366 3605.7 2603.1 -0.46989 1.2787e+05 1.1285e+05 2.9845 0.0014203 0.99858 0.0028405 0.028377 False -ACRC_p70800703 ACRC 1393.4/1472.3 1039.7/532.7 1432.8 786.2 -0.86508 65817 47077 2.9803 0.0014398 0.99856 0.0028796 0.028482 False -ADK_m75960590 ADK 208.72/65.71 292.1/350.17 137.22 321.14 1.2208 5956.2 3834.8 2.9701 0.99849 0.0015089 0.0030177 0.029556 True -ACTR1A_p104248866 ACTR1A 975.99/665.98 467.49/172.23 820.99 319.86 -1.3572 45822 28800 2.9531 0.0015732 0.99843 0.0031463 0.030516 False -ACSL6_m131329908 ACSL6 1011.3/866.66 557.71/419.98 938.97 488.84 -0.94029 9970 23558 2.9326 0.0016805 0.99832 0.0033609 0.032284 False -ACTN1_m69445700 ACTN1 699.66/571.86 1036.6/953.25 635.76 994.9 0.64526 5818.1 15265 2.9069 0.99817 0.0018254 0.0036508 0.034735 True -ADH5_m100006267 ADH5 1339.1/1610.8 2090.1/2003.8 1474.9 2047 0.47257 20324 38942 2.8988 0.99813 0.0018729 0.0037458 0.035196 True -ACO2_p41903797 ACO2 567.37/665.98 377.27/100.13 616.68 238.7 -1.3656 21633 17048 2.895 0.0018957 0.9981 0.0037915 0.035196 False -ACBD6_m180471338 ACBD6 1123/815.16 436.58/573.33 969.07 504.95 -0.93909 28364 25721 2.8939 0.0019025 0.9981 0.003805 0.035196 False -ACTL6B_m100253086 ACTL6B 637.92/925.27 1187.3/1186.7 781.6 1187 0.60222 20642 19686 2.8895 0.99807 0.0019291 0.0038582 0.035361 True -ADAR_m154574103 ADAR 132.29/133.2 278.22/658.58 132.74 468.4 1.8114 36168 13839 2.8533 0.99751 0.0024852 0.0049703 0.043556 True -ADCK3_p227149155 ADCK3 2275.4/2228.8 2932.4/2994.2 2252.1 2963.3 0.39579 1498 62378 2.8476 0.9978 0.0022023 0.0044046 0.040002 True -ADARB1_p46595645 ADARB1 1084.8/1005.2 1514.8/1497.4 1045 1506.1 0.52691 1658.6 26537 2.8306 0.99768 0.002323 0.004646 0.04146 True -ACAD9_m128598636 ACAD9 570.31/527.46 308.51/144.76 548.88 226.63 -1.2724 7162.1 12963 2.8305 0.0023241 0.99768 0.0046481 0.04146 False -ACVRL1_p52306296 ACVRL1 1331.7/1124.2 725.52/727.81 1227.9 726.67 -0.75606 10768 31756 2.8129 0.0024547 0.99755 0.0049093 0.043402 False -ACO2_m41895810 ACO2 2229.8/1791.9 1452.9/1241.6 2010.9 1347.3 -0.5774 59094 56356 2.7953 0.0025929 0.99741 0.0051858 0.045049 False -AHRR_m344006 AHRR 345.42/163.39 474.43/481.2 254.4 477.82 0.9067 8295.4 6439.2 2.7842 0.99731 0.0026854 0.0053709 0.045895 True -ADD3_m111860427 ADD3 283.69/518.58 682.62/695.77 401.13 689.2 0.77934 13837 10709 2.7837 0.99731 0.0026875 0.0053751 0.045895 True -AATF_p35306410 AATF 2096/1960.6 1573.4/629.97 2028.3 1101.7 -0.87996 2.2712e+05 1.1272e+05 2.76 0.0028899 0.99711 0.0057797 0.048932 False -ACVR1B_m52369170 ACVR1B 411.56/831.14 970.31/1637 621.35 1303.7 1.0679 1.5513e+05 61632 2.7484 0.99699 0.0030131 0.0060263 0.050502 True -ABI1_p27149701 ABI1 698.19/779.64 1791.1/1034.5 738.91 1412.8 0.93415 1.4477e+05 60285 2.7446 0.99697 0.0030332 0.0060664 0.050502 True -AHCY_p32883304 AHCY 1625.7/1410.1 2146.3/1986.6 1517.9 2066.5 0.44484 17992 40206 2.7358 0.99689 0.0031119 0.0062238 0.051385 True -AHNAK2_p105423983 AHNAK2 363.06/388.93 977.25/556.16 376 766.7 1.026 44496 20505 2.7285 0.9968 0.0031953 0.0063905 0.052329 True -ACO2_m41895784 ACO2 824.6/866.66 1135/1341.2 845.63 1238.1 0.54947 11074 20967 2.7103 0.99664 0.0033614 0.0067228 0.054602 True -AHNAK_p62303470 AHNAK 379.23/731.69 881.35/997.31 555.46 939.33 0.7569 34419 20230 2.6989 0.99652 0.0034785 0.0069571 0.056049 True -ADCK1_m78285413 ADCK1 1193.5/1355 1622/1905.9 1274.3 1764 0.46882 26674 33093 2.6918 0.99645 0.003553 0.007106 0.056334 True -ACVR2A_m148602752 ACVR2A 1403.7/996.31 745.71/644.28 1200 694.99 -0.78711 44070 35326 2.687 0.003605 0.9964 0.00721 0.056334 False -ADCK5_p145608311 ADCK5 601.18/742.35 1225.2/901.19 671.76 1063.2 0.66158 31227 21229 2.6865 0.99639 0.0036103 0.0072206 0.056334 True -ACHE_m100491773 ACHE 564.43/388.93 162.14/226.01 476.68 194.08 -1.292 8719.8 11080 2.685 0.0036263 0.99637 0.0072527 0.056334 False -AATK_p79104864 AATK 877.51/1136.6 1415.7/1455.1 1007.1 1435.4 0.51086 17169 25467 2.684 0.99636 0.0036372 0.0072744 0.056334 True -ADRB3_m37823894 ADRB3 1215.6/1864.7 2161.4/2267.6 1540.2 2214.5 0.52361 1.0817e+05 63299 2.6802 0.99632 0.0036785 0.0073569 0.056535 True -ADI1_m3523246 ADI1 801.08/665.98 1251.7/971.56 733.53 1111.6 0.59907 24180 19992 2.674 0.99625 0.0037472 0.0074944 0.056772 True -ADK_m76154033 ADK 557.08/685.52 875.04/1019.6 621.3 947.34 0.60779 9349.9 14879 2.6729 0.99624 0.0037603 0.0075206 0.056772 True -ACTL6A_p179287966 ACTL6A 1478.7/728.14 548.24/173.94 1103.4 361.09 -1.6088 1.7586e+05 77415 2.6712 0.0037791 0.99622 0.0075583 0.056772 False -AEBP1_m44144319 AEBP1 992.16/896.85 488.94/579.05 944.51 533.99 -0.82157 4300.8 23713 2.6658 0.0038398 0.99616 0.0076796 0.057253 False -AAAS_m53714382 AAAS 1034.8/1191.7 504.08/815.93 1113.2 660.01 -0.75331 30465 29136 2.6552 0.0039636 0.99604 0.0079272 0.058661 False -AATF_p35306466 AATF 277.81/435.11 182.33/0 356.46 91.164 -1.9555 14497 10179 2.6462 0.0040705 0.99593 0.008141 0.0598 False -ACTR3_m114688941 ACTR3 1148/1046 811.32/186.53 1097 498.93 -1.1351 1.0019e+05 52070 2.621 0.0043835 0.99562 0.0087669 0.063928 False -ACTL7A_p111624631 ACTL7A 659.97/717.48 377.9/325 688.73 351.45 -0.9686 1526.6 16687 2.611 0.0045142 0.99549 0.0090284 0.065358 False -AEN_m89169489 AEN 94.072/0 175.39/122.45 47.036 148.92 1.642 2913 1533.7 2.6015 0.99476 0.0052434 0.010487 0.071756 True -ACTL6A_m179287950 ACTL6A 380.7/387.16 191.79/90.977 383.93 141.38 -1.4348 2551.3 8710 2.6003 0.0046575 0.99534 0.0093151 0.066777 False -AFF1_p87967338 AFF1 620.29/641.12 825.83/1159.8 630.7 992.82 0.65375 27994 19418 2.5987 0.99532 0.0046791 0.0093581 0.066777 True -ACTL7A_p111624691 ACTL7A 335.13/497.27 601.24/779.88 416.2 690.56 0.72912 14550 11202 2.5922 0.99523 0.004768 0.0095361 0.067564 True -ACTR8_m53916057 ACTR8 992.16/1005.2 743.82/200.26 998.67 472.04 -1.0795 73906 41456 2.5865 0.0048472 0.99515 0.0096944 0.068202 False -AAK1_m69870056 AAK1 933.37/1255.6 1409.4/2011.2 1094.5 1710.3 0.64354 1.165e+05 57460 2.5691 0.9949 0.0050983 0.010197 0.071234 True -ADAD1_m123301366 ADAD1 526.21/557.65 897.76/765.01 541.93 831.38 0.61648 4652.6 12780 2.5604 0.99477 0.005228 0.010456 0.071756 True -ACAT2_p160183107 ACAT2 2034.3/2248.3 1099.6/1749.7 2141.3 1424.7 -0.58752 1.1711e+05 78351 2.5602 0.0052302 0.99477 0.01046 0.071756 False -AGL_p100327219 AGL 196.96/287.7 41.639/78.389 242.33 60.014 -1.9957 2396.1 5221.1 2.5479 0.0054181 0.99458 0.010836 0.073642 False -AHNAK_m62303488 AHNAK 399.8/504.37 781.04/643.13 452.09 712.09 0.65429 7488.2 10446 2.5438 0.99452 0.0054821 0.010964 0.074008 True -ADCK3_m227149163 ADCK3 1115.6/1063.8 1343.8/1829.3 1089.7 1586.5 0.54151 59592 38400 2.5353 0.99438 0.0056173 0.011235 0.075325 True -ACAD9_p128598545 ACAD9 829.01/1099.3 676.31/403.96 964.16 540.14 -0.83477 36810 28445 2.5141 0.0059667 0.99403 0.011933 0.079477 False -ACAD9_p128598592 ACAD9 921.61/774.31 499.03/469.76 847.96 484.4 -0.80653 5638.3 21032 2.5069 0.0060892 0.99391 0.012178 0.080572 False -ACTN4_p39138482 ACTN4 1362.6/1005.2 1596.2/1653.6 1183.9 1624.9 0.45648 32756 31246 2.4948 0.9937 0.0063007 0.012601 0.082247 True -AFTPH_m64778905 AFTPH 463.01/353.41 618.9/679.18 408.21 649.04 0.66768 3911.1 9324.9 2.4939 0.99368 0.0063167 0.012633 0.082247 True -ABT1_p26597221 ABT1 1597.7/1465.2 1065.6/992.73 1531.5 1029.2 -0.57298 5721.6 40607 2.4927 0.0063393 0.99366 0.012679 0.082247 False -AES_m3056311 AES 756.98/616.25 892.08/1161.5 686.62 1026.8 0.57988 23102 18787 2.4819 0.99347 0.0065342 0.013068 0.083824 True -AEBP2_p19615465 AEBP2 257.23/429.78 158.98/81.822 343.5 120.4 -1.5047 8932 8108.1 2.4813 0.0065448 0.99346 0.01309 0.083824 False -ADAP1_m994066 ADAP1 167.57/67.486 204.41/355.9 117.53 280.15 1.2462 8241.2 4303.9 2.4789 0.99316 0.0068396 0.013679 0.085409 True -ACTR3_m114684932 ACTR3 1024.5/1362.2 883.88/396.52 1193.3 640.2 -0.89735 87881 49801 2.4786 0.0065951 0.9934 0.01319 0.08393 False -A1CF_p52595881 A1CF 714.36/671.31 1119.8/906.91 692.83 1013.4 0.54792 11797 16797 2.4732 0.9933 0.0066957 0.013391 0.08445 True -ACLY_m40070097 ACLY 495.35/586.06 279.48/243.75 540.7 261.62 -1.0445 2376.6 12748 2.4719 0.0067205 0.99328 0.013441 0.08445 False -ADCY1_m45614243 ADCY1 1011.3/1211.2 485.15/843.4 1111.2 664.27 -0.74144 42077 32969 2.4616 0.0069165 0.99308 0.013833 0.085833 False -ACTL6A_p179287892 ACTL6A 415.97/669.53 750.76/1074.6 542.75 912.66 0.7487 42284 22629 2.459 0.99303 0.0069678 0.013936 0.085936 True -ADAMTS5_p28338585 ADAMTS5 973.05/939.48 538.15/613.38 956.27 575.76 -0.73094 1696.7 24042 2.454 0.0070641 0.99294 0.014128 0.086472 False -AGL_m100327128 AGL 122/248.63 318.6/366.2 185.32 342.4 0.88213 4575.4 4108.1 2.4508 0.99286 0.0071411 0.014282 0.086472 True -AHNAK2_m105444536 AHNAK2 382.17/454.64 707.86/608.8 418.4 658.33 0.65266 3766.3 9584.3 2.4507 0.99287 0.0071284 0.014257 0.086472 True -ADCK1_m78285328 ADCK1 662.91/1008.7 1101.5/1694.8 835.82 1398.2 0.74157 1.1789e+05 53095 2.4405 0.99266 0.0073351 0.01467 0.088286 True -ADAP1_m994042 ADAP1 292.5/369.4 454.24/709.5 330.95 581.87 0.81221 17768 10845 2.4094 0.99201 0.0079943 0.015989 0.095645 True -ADNP2_p77875475 ADNP2 248.41/106.56 286.42/461.18 177.48 373.8 1.0703 12665 6683.6 2.4013 0.99171 0.0082915 0.016583 0.097623 True -AEBP1_m44144326 AEBP1 1149.4/1149 1616.3/1506.6 1149.2 1561.4 0.44187 3013.3 29499 2.4 0.9918 0.0081976 0.016395 0.097493 True -ACVR2B_m38518828 ACVR2B 404.21/1095.8 1399.9/1221 749.99 1310.5 0.80435 1.2756e+05 54751 2.3954 0.99169 0.0083063 0.016613 0.097623 True -ABI1_m27149751 ABI1 1233.2/868.44 720.48/531.56 1050.8 626.02 -0.74633 42189 31864 2.3798 0.0086604 0.99134 0.017321 0.10099 False -ABCB8_m150725643 ABCB8 598.24/1035.4 1096.5/1688.5 816.81 1392.5 0.76888 1.3539e+05 58581 2.3785 0.99131 0.0086939 0.017388 0.10099 True -AGFG1_p228337221 AGFG1 449.78/428 713.54/641.99 438.89 677.76 0.62576 1398.4 10108 2.3759 0.99125 0.0087524 0.017505 0.10108 True -ADRB3_p37823813 ADRB3 1267/703.28 528.05/453.17 985.15 490.61 -1.0043 80857 43520 2.3707 0.0088784 0.99112 0.017757 0.10195 False -ACTRT3_p169487206 ACTRT3 35.277/406.69 451.09/596.79 220.98 523.94 1.2417 39794 16406 2.3652 0.99059 0.0094075 0.018815 0.1068 True -ACD_m67694376 ACD 1855/1420.8 1083.9/1183.8 1637.9 1133.9 -0.53019 49635 45718 2.3572 0.0092066 0.99079 0.018413 0.10511 False -AFF3_p100623773 AFF3 1902/1825.7 1573.4/850.83 1863.8 1212.1 -0.62031 1.32e+05 77686 2.3382 0.0096884 0.99031 0.019377 0.10936 False -AHNAK_m62303548 AHNAK 868.69/980.32 721.11/327.86 924.51 524.48 -0.8166 41776 29362 2.3345 0.0097848 0.99022 0.01957 0.10983 False -ACTRT3_m169487242 ACTRT3 2171/1610.8 1054.8/1460.2 1890.9 1257.5 -0.58809 1.1954e+05 74078 2.3271 0.0099808 0.99002 0.019962 0.11141 False -ACTR1A_m104250350 ACTR1A 417.44/223.77 119.24/110.43 320.61 114.83 -1.4732 9396.8 7884.1 2.323 0.010089 0.98991 0.020177 0.11198 False -ADCK3_p227149104 ADCK3 1628.6/1669.4 2129.3/2142.2 1649 2135.7 0.37295 457.83 44091 2.3181 0.98978 0.010222 0.020445 0.11284 True -AHNAK2_m105423829 AHNAK2 737.88/879.09 561.49/402.82 808.48 482.15 -0.74452 11280 19945 2.3107 0.010425 0.98957 0.020851 0.11445 False -ACRC_p70814200 ACRC 1940.2/1289.3 942.55/1103.7 1614.8 1023.1 -0.65782 1.1241e+05 66186 2.2997 0.010732 0.98927 0.021464 0.11689 False -AGPAT3_p45379592 AGPAT3 1078.9/451.09 300.3/251.19 764.99 275.75 -1.4688 99135 45548 2.2982 0.010774 0.98923 0.021549 0.11689 False -ADNP_p49511011 ADNP 427.73/573.63 655.49/899.47 500.68 777.48 0.63389 20202 14536 2.2958 0.98916 0.010843 0.021685 0.11689 True -ADCK2_m140373210 ADCK2 862.81/628.69 1499.6/940.67 745.75 1220.1 0.70954 91813 42758 2.2942 0.98911 0.010891 0.021782 0.11689 True -ADH5_m100003154 ADH5 560.02/753 262.45/456.6 656.51 359.53 -0.86691 18734 16792 2.2919 0.010956 0.98904 0.021913 0.11689 False -AGPAT5_m6566202 AGPAT5 286.62/570.08 193.68/129.89 428.35 161.78 -1.3992 21104 13593 2.2904 0.010999 0.989 0.021998 0.11689 False -ADCK4_p41220480 ADCK4 2523.8/2445.5 2153.2/1516.9 2484.6 1835 -0.43701 1.0278e+05 80651 2.2873 0.011088 0.98891 0.022176 0.11722 False -AGAP3_p150784007 AGAP3 707.01/635.79 837.19/1465.4 671.4 1151.3 0.77709 99916 44119 2.2846 0.98883 0.011175 0.02235 0.11751 True -ACBD6_p180471279 ACBD6 792.26/1243.2 681.99/462.32 1017.7 572.16 -0.82974 62892 38142 2.2814 0.011263 0.98874 0.022525 0.11782 False -ABL2_p179100524 ABL2 692.31/472.4 755.81/1107.2 582.36 931.49 0.67671 42954 23548 2.2752 0.98855 0.011449 0.022898 0.11914 True -ACIN1_p23538701 ACIN1 405.68/268.17 170.34/109.86 336.93 140.1 -1.26 5642.2 7532.4 2.2695 0.011618 0.98838 0.023235 0.12027 False -ADAR_p154574114 ADAR 99.951/195.35 38.484/20.026 147.65 29.255 -2.2967 2360.6 3009.6 2.259 0.011943 0.98806 0.023885 0.123 False -ADAM12_p128076641 ADAM12 1128.9/1333.7 1475.7/1837.3 1231.3 1656.5 0.42762 43186 35631 2.2524 0.98785 0.012149 0.024297 0.12417 True -ABLIM2_p8108299 ABLIM2 939.25/1484.7 1625.2/1782.3 1212 1703.8 0.49102 80553 47716 2.2514 0.98782 0.012181 0.024362 0.12417 True -ACVR2A_p148602720 ACVR2A 1412.5/715.71 1485.1/1809.8 1064.1 1647.5 0.6301 1.4775e+05 67303 2.2485 0.98773 0.012271 0.024542 0.1243 True -ACTN4_m39138430 ACTN4 163.16/264.62 376.01/354.75 213.89 365.38 0.76978 2686.5 4544.2 2.2474 0.98768 0.012318 0.024636 0.1243 True -AATF_m35306509 AATF 367.47/571.86 300.3/116.72 469.66 208.51 -1.1676 18869 13556 2.2438 0.012422 0.98758 0.024844 0.12472 False -ACAD11_p132378460 ACAD11 1621.3/1113.5 816.37/951.54 1367.4 883.95 -0.62881 69020 46870 2.233 0.012773 0.98723 0.025547 0.12761 False -AHNAK_m62303560 AHNAK 704.07/959.01 1027.7/1504.8 831.54 1266.3 0.60614 73159 38106 2.2271 0.98703 0.012972 0.025943 0.12879 True -ACTN1_m69392359 ACTN1 545.32/353.41 897.76/593.35 449.37 745.55 0.72914 32373 17709 2.2257 0.98698 0.013021 0.026042 0.12879 True -ADRBK2_m26057580 ADRBK2 610/815.16 1209.4/892.6 712.58 1051 0.56 35616 23426 2.2112 0.98649 0.013512 0.027024 0.13299 True -ABCB8_p150725612 ABCB8 368.94/190.03 700.29/391.94 279.48 546.12 0.96394 31771 14669 2.2015 0.986 0.013999 0.027998 0.13711 True -ABCF1_p30545587 ABCF1 48.506/248.63 665.59/248.9 148.57 457.24 1.6153 53420 19827 2.1922 0.9834 0.016603 0.033206 0.15509 True -ACVRL1_p52306281 ACVRL1 1195/896.85 1687.6/1273.1 1045.9 1480.4 0.50076 65182 39436 2.1877 0.98565 0.014347 0.028694 0.13983 True -AHCY_m32883210 AHCY 301.32/657.1 228.38/137.32 479.21 182.85 -1.3851 33717 18670 2.1749 0.01482 0.98518 0.02964 0.14325 False -ADAMTS5_p28338667 ADAMTS5 1115.6/923.49 1207.5/1932.8 1019.6 1570.2 0.62248 1.4075e+05 64128 2.1743 0.98516 0.014841 0.029683 0.14325 True -ADNP_p49520498 ADNP 690.84/767.21 1040.3/993.31 729.02 1016.8 0.47947 2011.1 17776 2.1586 0.98456 0.015442 0.030884 0.14833 True -ACSL6_p131329830 ACSL6 770.21/735.24 519.85/403.96 752.73 461.91 -0.70332 3663.5 18421 2.1428 0.016066 0.98393 0.032133 0.15359 False -AFTPH_m64778617 AFTPH 415.97/293.03 242.89/37.192 354.5 140.04 -1.3337 14357 10099 2.1391 0.016214 0.98379 0.032429 0.15398 False -ADCK5_p145603106 ADCK5 1483.1/1340.8 2315.4/1616.4 1412 1965.9 0.47718 1.2719e+05 67129 2.1379 0.98374 0.016261 0.032523 0.15398 True -ACLY_m40069993 ACLY 1268.5/1411.9 1136.2/603.65 1340.2 869.94 -0.62286 76050 48686 2.1312 0.016536 0.98346 0.033072 0.15509 False -ADNP_m49520507 ADNP 959.83/1397.7 1429.6/2811.1 1178.7 2120.4 0.84651 5.2508e+05 1.9526e+05 2.1309 0.98339 0.016611 0.033222 0.15509 True -AAK1_m69870103 AAK1 712.89/802.73 576.63/359.9 757.81 468.27 -0.69332 13761 18559 2.1254 0.016779 0.98322 0.033557 0.15592 False -ADCK1_p78285416 ADCK1 826.07/797.4 977.88/2066.7 811.73 1522.3 0.90634 2.966e+05 1.1222e+05 2.1211 0.98291 0.017088 0.034175 0.15806 True -ACLY_p40070062 ACLY 512.98/269.94 205.67/126.45 391.46 166.06 -1.2322 16336 11379 2.1158 0.017179 0.98282 0.034358 0.15817 False -AGL_m100327263 AGL 758.45/463.52 410.08/210.56 610.99 310.32 -0.9751 31698 20302 2.1104 0.017414 0.98259 0.034828 0.1596 False -ADAM12_p128019039 ADAM12 1824.1/815.16 661.8/605.37 1319.6 633.59 -1.0573 2.5529e+05 1.0803e+05 2.0879 0.018404 0.9816 0.036807 0.16759 False -ACBD6_p180471344 ACBD6 1040.7/1292.9 1392.4/1703.4 1166.8 1547.9 0.40746 40086 33362 2.0865 0.98153 0.018469 0.036937 0.16759 True -ADRM1_p60878615 ADRM1 477.71/841.8 176.65/448.02 659.75 312.33 -1.0764 51551 27789 2.0849 0.018538 0.98146 0.037075 0.16759 False -ACTL6B_m100253443 ACTL6B 1114.2/1284 1689.5/1439.6 1199.1 1564.6 0.38355 22826 30927 2.0782 0.98116 0.018844 0.037687 0.16959 True -ADRB1_m115803922 ADRB1 230.77/223.77 388.63/354.75 227.27 371.69 0.70724 299.14 4861.5 2.0713 0.98082 0.019175 0.038351 0.1717 True -ABCC1_m16101705 ABCC1 1283.2/1053.1 832.77/786.18 1168.2 809.48 -0.52864 13775 30041 2.0695 0.019249 0.98075 0.038498 0.1717 False -ACTR8_m53916047 ACTR8 2484.1/2349.6 2172.8/910.91 2416.8 1541.8 -0.64812 4.026e+05 1.7919e+05 2.067 0.019366 0.98063 0.038732 0.17197 False -AATF_m35306475 AATF 724.65/1086.9 695.24/307.26 905.76 501.25 -0.85232 70435 38567 2.0598 0.019707 0.98029 0.039415 0.17423 False -ABLIM2_p8108286 ABLIM2 936.31/996.31 1115.4/1572.9 966.31 1344.2 0.47574 53230 33959 2.0505 0.97984 0.020158 0.040316 0.17742 True -AGPAT3_p45379648 AGPAT3 1030.4/657.1 589.25/411.4 843.74 500.32 -0.75276 42742 28191 2.0453 0.020411 0.97959 0.040821 0.1784 False -ADRBK1_p67034166 ADRBK1 1270/1213 1591.1/1625 1241.5 1608 0.373 1099.3 32146 2.0446 0.97955 0.020448 0.040896 0.1784 True -ADD3_p111860505 ADD3 977.46/845.35 620.8/585.34 911.41 603.07 -0.59497 4677.7 22790 2.0425 0.020553 0.97945 0.041105 0.17854 False -AFTPH_m64778773 AFTPH 60.265/161.61 231.54/191.68 110.94 211.61 0.9255 2964.9 2448.4 2.0345 0.97878 0.021215 0.042431 0.1835 True -ADH5_p100002510 ADH5 443.9/241.53 451.72/726.67 342.71 589.19 0.77998 29138 14830 2.024 0.97846 0.02154 0.04308 0.1855 True -ACTR3_p114684941 ACTR3 952.48/1094 1239.1/1457.9 1023.2 1348.5 0.39788 16980 25923 2.0202 0.97832 0.021681 0.043363 0.18592 True -ADCY1_m45614323 ADCY1 709.95/912.84 1003.7/1190.1 811.39 1096.9 0.43455 18976 20025 2.0179 0.9782 0.021802 0.043604 0.18616 True -ABL1_m133729449 ABL1 599.71/374.72 1384.2/601.36 487.22 992.77 1.0254 1.6585e+05 62853 2.0165 0.97754 0.022456 0.044913 0.19012 True -AAK1_m69870137 AAK1 1330.2/522.13 323.02/456.6 926.18 389.81 -1.2464 1.6772e+05 71374 2.0126 0.022081 0.97792 0.044162 0.18773 False -A1CF_m52603847 A1CF 0/88.797 91.479/152.2 44.399 121.84 1.4361 2893 1492.1 2.0048 0.97429 0.025711 0.051423 0.20798 True -ADRB1_m115804047 ADRB1 637.92/545.22 385.47/322.14 591.57 353.81 -0.73996 3151.5 14089 2.0031 0.022583 0.97742 0.045166 0.19038 False -AGPAT3_m45379563 AGPAT3 263.11/250.41 51.102/164.79 256.76 107.95 -1.2424 3271.4 5567.8 1.9995 0.022775 0.97723 0.04555 0.19119 False -ACO2_m41865159 ACO2 1192.1/879.09 852.33/377.07 1035.6 614.7 -0.75153 80957 44500 1.9952 0.023012 0.97699 0.046024 0.19237 False -ADK_m76153995 ADK 1011.3/1166.8 652.97/860.56 1089 756.77 -0.52455 16820 27785 1.9934 0.023111 0.97689 0.046221 0.1924 False -ADRA1A_p26722406 ADRA1A 333.66/557.65 632.15/680.9 445.65 656.52 0.55788 13137 11233 1.9896 0.97668 0.023318 0.046635 0.19331 True -ABCC1_m16101799 ABCC1 730.53/907.51 491.46/585.34 819.02 538.4 -0.60429 10034 20235 1.9727 0.024264 0.97574 0.048528 0.19967 False -AFF4_m132272833 AFF4 1365.5/1925.1 1092.1/1252.5 1645.3 1172.3 -0.48869 84728 57563 1.9716 0.024328 0.97567 0.048656 0.19967 False -ADCY1_p45614309 ADCY1 389.52/195.35 176.02/37.764 292.44 106.89 -1.4435 14203 9024.2 1.9706 0.024384 0.97562 0.048768 0.19967 False -ADNP2_m77891006 ADNP2 0/0 15.772/168.79 4.4399 92.283 4.1 5853.9 1992.3 1.968 0.95454 0.045461 0.090922 0.29049 True -AHCTF1_m247068886 AHCTF1 1095.1/882.65 837.82/365.05 988.85 601.44 -0.7164 67157 39023 1.9612 0.024929 0.97507 0.049858 0.20315 False -ADI1_p3523164 ADI1 1120/1216.5 638.46/1000.2 1168.3 819.32 -0.51137 35036 31708 1.9597 0.025013 0.97499 0.050026 0.20315 False -AGTPBP1_m88307696 AGTPBP1 0/362.29 437.84/382.79 181.15 410.31 1.1751 33572 13709 1.9573 0.97321 0.026791 0.053581 0.21411 True -ADI1_p3517642 ADI1 261.64/399.59 406.29/1138.1 330.61 772.18 1.2213 1.3863e+05 51128 1.9529 0.97261 0.027386 0.054772 0.21627 True -A1CF_m52596056 A1CF 618.82/653.55 357.08/434.29 636.18 395.68 -0.68371 1791.6 15276 1.9458 0.025838 0.97416 0.051677 0.20817 False -ACAD9_p128598519 ACAD9 636.45/754.78 581.05/224.87 695.62 402.96 -0.78616 35217 22987 1.9303 0.026785 0.97322 0.053569 0.21411 False -AGAP3_p150783825 AGAP3 326.31/635.79 960.85/615.67 481.05 788.26 0.71131 53731 25373 1.9286 0.97308 0.026923 0.053847 0.21431 True -ADAM10_m58974490 ADAM10 612.94/699.72 426.48/401.67 656.33 414.08 -0.66324 2036.9 15816 1.9263 0.027032 0.97297 0.054065 0.21433 False -ADPRHL2_m36554620 ADPRHL2 351.3/298.36 244.79/74.384 324.83 159.58 -1.0208 7959.8 7474.8 1.9126 0.0279 0.9721 0.055799 0.21946 False -ACTL7A_m111624684 ACTL7A 533.56/348.09 606.92/658.58 440.82 632.75 0.52044 9267.8 10157 1.9043 0.97157 0.028434 0.056869 0.2226 True -ACTRT3_m169487289 ACTRT3 1117.1/532.78 474.43/389.66 824.94 432.04 -0.93153 87153 42649 1.903 0.028521 0.97148 0.057042 0.2226 False -AGL_p100327060 AGL 730.53/461.75 329.96/386.22 596.14 358.09 -0.73372 18852 15758 1.8964 0.028956 0.97104 0.057912 0.22511 False -ADAM12_p128018991 ADAM12 51.445/69.262 83.277/222.58 60.354 152.93 1.327 4930.6 2385.8 1.8953 0.96745 0.032554 0.065108 0.24032 True -ADCK5_p145608305 ADCK5 376.29/619.81 376.64/94.41 498.05 235.53 -1.0772 34739 19336 1.8904 0.029353 0.97065 0.058706 0.22652 False -ADIRF_p88728321 ADIRF 1647.7/760.11 685.78/608.23 1203.9 647 -0.89486 1.9847e+05 86867 1.8899 0.029387 0.97061 0.058775 0.22652 False -ABCF1_p30545610 ABCF1 1975.5/1797.3 1628.3/1289.7 1886.4 1459 -0.37041 36611 51211 1.8885 0.029477 0.97052 0.058955 0.22652 False -ACBD6_p180471350 ACBD6 607.06/538.11 754.54/830.24 572.58 792.39 0.46802 2620.6 13587 1.8857 0.97033 0.029667 0.059334 0.22708 True -ABL1_p133729492 ABL1 1105.3/1172.1 1019.5/401.1 1138.7 710.31 -0.68015 96725 51708 1.8841 0.029778 0.97022 0.059555 0.22708 False -AGAP2_p58129148 AGAP2 762.86/1189.9 622.06/660.3 976.37 641.18 -0.60594 45952 31721 1.882 0.029916 0.97008 0.059832 0.22727 False -ADH7_m100350762 ADH7 1856.4/1578.8 1422.7/1207.9 1717.6 1315.3 -0.38481 30802 46138 1.8732 0.030518 0.96948 0.061036 0.22984 False -AFAP1L2_p116100409 AFAP1L2 1344.9/1133.1 2324.8/1356.6 1239 1840.7 0.57073 2.4557e+05 1.0324e+05 1.8728 0.96945 0.030551 0.061102 0.22984 True -AES_m3061159 AES 565.9/658.88 1000.6/715.8 612.39 858.2 0.48618 22438 17241 1.872 0.9694 0.0306 0.0612 0.22984 True -ABI1_p27112164 ABI1 1709.5/1655.2 1220.8/1354.9 1682.3 1287.8 -0.38523 5235.8 45083 1.8578 0.031596 0.9684 0.063191 0.23643 False -ADRB1_p115803909 ADRB1 577.66/634.01 706.6/969.28 605.84 837.94 0.46725 18044 15660 1.8547 0.96818 0.031817 0.063635 0.23721 True -ABCB8_p150725605 ABCB8 149.93/259.29 880.72/244.89 204.61 562.81 1.4553 1.0406e+05 37570 1.848 0.9622 0.037804 0.075607 0.26117 True -ACSL6_m131326625 ACSL6 640.86/190.03 897.13/587.06 415.45 742.09 0.83541 74849 31289 1.8466 0.96729 0.032708 0.065415 0.24032 True -ABLIM2_m8108274 ABLIM2 546.79/1392.3 1411.3/1554.6 969.57 1483 0.61255 1.8387e+05 77568 1.8434 0.96735 0.032647 0.065294 0.24032 True -ACTRT3_m169487267 ACTRT3 634.98/676.64 847.28/927.51 655.81 887.4 0.43573 2042.6 15802 1.8423 0.96728 0.032716 0.065431 0.24032 True -ADD1_p2877730 ADD1 194.02/24.863 238.48/209.42 109.44 223.95 1.0263 7364.8 3893.2 1.8351 0.96538 0.034618 0.069237 0.2506 True -ADD3_p111860500 ADD3 515.92/1049.6 1134.3/1131.8 782.75 1133.1 0.53302 71200 36560 1.8321 0.96653 0.033472 0.066944 0.24497 True -AHCTF1_m247068867 AHCTF1 748.16/488.39 239.74/477.77 618.27 358.75 -0.78357 31036 20211 1.8256 0.033958 0.96604 0.067917 0.24762 False -ADI1_m3523251 ADI1 1561/1820.3 2134.3/2023.2 1690.7 2078.8 0.29797 19899 45333 1.8228 0.96583 0.03417 0.06834 0.24826 True -AGFG1_m228337248 AGFG1 983.34/671.31 675.68/335.87 827.33 505.78 -0.70885 53210 31379 1.8152 0.034743 0.96526 0.069486 0.2506 False -ABT1_p26597313 ABT1 30.867/179.37 238.48/184.81 105.12 211.65 1.0028 6233.2 3453 1.8128 0.96374 0.036264 0.072527 0.25877 True -AATK_m79102298 AATK 1067.1/1417.2 1601.2/1531.2 1242.2 1566.2 0.33415 31865 32166 1.8066 0.96459 0.035412 0.070824 0.25451 True -ADCK4_m41220284 ADCK4 1234.7/1300 1357.7/2232.7 1267.3 1795.2 0.50197 1.9246e+05 86082 1.799 0.96399 0.03601 0.07202 0.25788 True -ACVR2A_p148653859 ACVR2A 248.41/621.58 605.02/783.89 434.99 694.46 0.67365 42813 20943 1.7929 0.96346 0.036544 0.073087 0.25892 True -ADPRHL2_m36554557 ADPRHL2 1597.7/1046 759.59/1043.1 1321.9 901.34 -0.55195 96190 55045 1.7925 0.036525 0.96347 0.07305 0.25892 False -ADD1_p2877736 ADD1 1103.9/809.83 769.69/554.44 956.85 662.06 -0.53065 33197 27105 1.7906 0.036682 0.96332 0.073364 0.25898 False -ACTRT3_m169487273 ACTRT3 806.96/721.03 177.91/685.47 764 431.69 -0.82211 66251 34569 1.7875 0.036926 0.96307 0.073853 0.25926 False -ACVR2A_p148653946 ACVR2A 952.48/467.07 919.21/1190.1 709.78 1054.7 0.5707 77255 37255 1.7869 0.96302 0.036982 0.073964 0.25926 True -ADAD1_p123301335 ADAD1 790.79/408.47 375.38/290.67 599.63 333.02 -0.84652 38337 22314 1.7851 0.037122 0.96288 0.074244 0.25933 False -ABHD14B_p52004061 ABHD14B 389.52/605.6 377.27/185.39 497.56 281.33 -0.82038 20878 14707 1.7832 0.037275 0.96273 0.074549 0.25949 False -ACVR1_p158637068 ACVR1 689.37/1438.5 1443.5/1606.1 1063.9 1524.8 0.51878 1.4692e+05 67022 1.7801 0.96247 0.037528 0.075055 0.26035 True -ACVR2B_m38495825 ACVR2B 2063.7/2365.6 1987.3/1531.7 2214.6 1759.5 -0.33172 74667 65705 1.7755 0.037908 0.96209 0.075815 0.26117 False -ABCC1_m16043636 ABCC1 70.554/369.4 355.19/467.47 219.98 411.33 0.89992 25479 11618 1.7753 0.96128 0.038724 0.077448 0.26406 True -ABCF1_p30545638 ABCF1 213.13/376.5 404.4/482.92 294.82 443.66 0.588 8213.8 7066.6 1.7706 0.96168 0.03832 0.076639 0.263 True -ADIRF_p88728315 ADIRF 702.6/1010.5 1291.4/1017.3 856.56 1154.4 0.43007 42485 28341 1.7691 0.96156 0.038437 0.076873 0.263 True -ACHE_m100491721 ACHE 1075.9/873.77 824.57/567.03 974.86 695.8 -0.48592 26801 25309 1.7541 0.039707 0.96029 0.079415 0.26985 False -AEBP1_p44144347 AEBP1 1039.2/994.53 685.78/786.75 1016.9 736.26 -0.46529 3047.7 25743 1.7489 0.040157 0.95984 0.080313 0.27198 False -ABCB8_p150725600 ABCB8 438.02/399.59 220.81/275.79 418.8 248.3 -0.75183 1125 9594.5 1.7408 0.04086 0.95914 0.081719 0.2758 False -ADAD1_p123301358 ADAD1 1749.1/1907.4 2041.6/2387.1 1828.3 2214.3 0.27628 36115 49457 1.7361 0.95873 0.041273 0.082545 0.27703 True -ACVR1B_m52369237 ACVR1B 1472.8/1536.2 1218.2/1098.6 1504.5 1158.4 -0.37684 4584 39812 1.7345 0.041414 0.95859 0.082828 0.27703 False -AGBL5_m27276006 AGBL5 1133.3/1394.1 639.09/1131.2 1263.7 885.15 -0.51317 77554 47709 1.7331 0.04154 0.95846 0.083081 0.27703 False -AGTPBP1_p88296203 AGTPBP1 438.02/340.98 458.66/679.75 389.5 569.2 0.54615 14575 10759 1.7325 0.9584 0.041596 0.083193 0.27703 True -AGPAT3_m45379578 AGPAT3 101.42/156.28 206.93/226.01 128.85 216.47 0.74396 843.48 2586.8 1.7228 0.95729 0.042708 0.085416 0.28166 True -ADK_m75960552 ADK 1472.8/1829.2 1263/1315.4 1651 1289.2 -0.35659 32444 44151 1.7218 0.042557 0.95744 0.085114 0.28166 False -AFF2_m147967463 AFF2 604.12/911.06 929.93/1103.7 757.59 1016.8 0.42411 31106 22737 1.7193 0.95722 0.042784 0.085567 0.28166 True -ACTR3_p114691883 ACTR3 542.38/776.09 418.28/466.9 659.24 442.59 -0.57375 14246 15893 1.7185 0.042856 0.95714 0.085712 0.28166 False -AHRR_p344036 AHRR 338.07/607.37 378.53/65.229 472.72 221.88 -1.0878 42671 21542 1.7157 0.043111 0.95689 0.086221 0.28174 False -AFF3_m100623802 AFF3 626.17/191.8 130.59/185.39 408.98 157.99 -1.3666 47918 22202 1.7153 0.043149 0.95685 0.086298 0.28174 False -ADRB2_p148206408 ADRB2 1074.5/754.78 1548.8/1021.3 914.63 1285.1 0.49016 95113 46958 1.7096 0.95633 0.043672 0.087344 0.28422 True -AAAS_p53714367 AAAS 474.77/582.51 411.34/268.35 528.64 339.85 -0.63589 8013.4 12432 1.6932 0.045206 0.95479 0.090411 0.29049 False -ADIRF_p88729975 ADIRF 89.662/209.56 208.82/304.4 149.61 256.61 0.77437 5877.7 3995.3 1.6928 0.95435 0.045653 0.091307 0.29049 True -AAK1_p69870070 AAK1 1456.6/1275.1 1129.3/962.41 1365.9 1045.9 -0.38484 15199 35751 1.6926 0.045266 0.95473 0.090532 0.29049 False -AGAP2_m58129187 AGAP2 917.2/768.99 1056.7/1118 843.09 1087.4 0.36672 6431.3 20897 1.69 0.95448 0.045519 0.091037 0.29049 True -ACP1_p272054 ACP1 645.27/429.78 154.57/448.59 537.53 301.58 -0.8317 33222 19517 1.6895 0.045561 0.95444 0.091122 0.29049 False -ACTR1A_p104250304 ACTR1A 654.09/1040.7 1092.1/1155.8 847.4 1123.9 0.40703 38383 26805 1.6891 0.9544 0.045603 0.091206 0.29049 True -ACD_p67694355 ACD 752.57/795.62 1094.6/917.21 774.1 1005.9 0.37747 8329.9 19004 1.6815 0.95367 0.046333 0.092665 0.29388 True -ADRA1B_m159343996 ADRA1B 748.16/523.9 919.84/777.02 636.03 848.43 0.41512 17672 16072 1.6754 0.95307 0.046933 0.093865 0.296 True -ABI1_m27149710 ABI1 636.45/1191.7 722.37/328.43 914.06 525.4 -0.7977 1.1586e+05 53863 1.675 0.046963 0.95304 0.093926 0.296 False -AES_m3061215 AES 523.27/905.73 1206.3/844.54 714.5 1025.4 0.52056 69279 34682 1.6694 0.95248 0.04752 0.09504 0.29857 True -ADARB2_m1779251 ADARB2 742.28/598.49 943.81/820.51 670.39 882.16 0.39552 8969.8 16193 1.6642 0.95196 0.048038 0.096076 0.30078 True -ABLIM2_m8108316 ABLIM2 291.03/307.24 154.57/173.94 299.14 164.26 -0.86092 159.5 6598.9 1.6615 0.048306 0.95169 0.096612 0.30078 False -ADPRHL2_p36554606 ADPRHL2 257.23/245.08 52.995/203.12 251.15 128.06 -0.96627 5671.6 5512.4 1.6613 0.048324 0.95168 0.096648 0.30078 False -ACLY_p40069998 ACLY 877.51/715.71 538.15/594.5 796.61 566.32 -0.49151 7339.1 19619 1.6441 0.050079 0.94992 0.10016 0.31074 False -ADAP1_m994091 ADAP1 878.98/1218.3 358.98/956.11 1048.6 657.55 -0.67254 1.1793e+05 57069 1.6372 0.050797 0.9492 0.10159 0.31351 False -ADRBK2_p26057601 ADRBK2 1456.6/1681.8 1731.2/2079.3 1569.2 1905.2 0.27975 42978 42141 1.6368 0.94916 0.050839 0.10168 0.31351 True -ACVR2B_p38518841 ACVR2B 587.95/495.49 733.09/717.52 541.72 725.3 0.42037 2197.8 12775 1.6243 0.94784 0.052157 0.10431 0.32065 True -AGPAT5_p6566262 AGPAT5 696.72/486.61 950.12/681.47 591.66 815.79 0.46276 29080 19088 1.6223 0.94763 0.052374 0.10475 0.32099 True -ADAMTS5_p28338596 ADAMTS5 840.77/571.86 373.49/573.33 706.31 473.41 -0.57622 28062 20795 1.6151 0.053144 0.94686 0.10629 0.32472 False -ACP1_m272137 ACP1 1730/722.81 789.87/605.37 1226.4 697.62 -0.81305 2.6214e+05 1.0852e+05 1.6061 0.054128 0.94587 0.10826 0.3284 False -ADRBK2_m25961078 ADRBK2 316.02/419.12 497.77/529.84 367.57 513.81 0.48208 2914.6 8298.3 1.6053 0.94578 0.054217 0.10843 0.3284 True -ACVRL1_p52306245 ACVRL1 836.36/973.22 1095.2/1197 904.79 1146.1 0.34076 7272.6 22606 1.6051 0.94576 0.05424 0.10848 0.3284 True -AGAP2_m58128454 AGAP2 1212.6/1152.6 897.76/908.62 1182.6 903.19 -0.3885 931.14 30454 1.6012 0.054667 0.94533 0.10933 0.32994 False -ADRBK1_p67034160 ADRBK1 1018.6/717.48 1070.6/1144.4 868.05 1107.5 0.35108 24030 22402 1.5998 0.94517 0.054825 0.10965 0.32994 True -ACVR2B_p38518817 ACVR2B 1265.6/1760 1651/2271.6 1512.8 1961.3 0.37441 1.5737e+05 79160 1.5942 0.94456 0.055445 0.11089 0.3319 True -AGAP3_p150783857 AGAP3 1328.8/1038.9 1346.3/1596.4 1183.8 1471.4 0.31342 36634 32538 1.5939 0.94452 0.055483 0.11097 0.3319 True -ACTN4_p39138459 ACTN4 1575.7/2017.5 1196.8/1602.1 1796.6 1399.5 -0.36017 89860 62290 1.5912 0.05578 0.94422 0.11156 0.33208 False -ADPRHL2_m36554512 ADPRHL2 495.35/809.83 791.77/982.44 652.59 887.1 0.44234 33814 21748 1.5902 0.94411 0.055895 0.11179 0.33208 True -AFF2_p147967406 AFF2 1043.6/1051.4 1372.8/1240.5 1047.5 1306.7 0.31867 4392.6 26607 1.5888 0.94395 0.056048 0.1121 0.33208 True -ACTRT3_m169487247 ACTRT3 432.14/834.7 288.32/464.61 633.42 376.46 -0.7491 48282 26229 1.587 0.056259 0.94374 0.11252 0.33208 False -ACVR1B_p52369196 ACVR1B 405.68/284.15 173.49/237.46 344.92 205.47 -0.74446 4715.3 7731.4 1.5862 0.056343 0.94366 0.11269 0.33208 False -AFF3_m100625275 AFF3 521.8/237.98 606.92/516.11 379.89 561.51 0.56251 22201 13139 1.5845 0.94343 0.05657 0.11314 0.33243 True -AFMID_p76183473 AFMID 383.64/428 833.41/426.85 405.82 630.13 0.63354 41814 20114 1.5816 0.94301 0.056994 0.11399 0.33371 True -ADARB1_m46595779 ADARB1 665.85/174.04 191.79/144.76 419.95 168.28 -1.3143 61022 26756 1.5794 0.057122 0.94288 0.11424 0.33371 False -AEN_m89169460 AEN 1606.6/1554 1376.6/1138.1 1580.3 1257.3 -0.32956 14917 42050 1.5748 0.057652 0.94235 0.1153 0.33542 False -ACVR1_p158655928 ACVR1 288.09/307.24 536.89/335.87 297.67 436.38 0.55035 10194 7773.1 1.5733 0.94216 0.057844 0.11569 0.33542 True -ACTN1_m69392382 ACTN1 684.96/914.61 846.65/1418.4 799.79 1132.5 0.50135 94919 44777 1.5725 0.94208 0.057918 0.11584 0.33542 True -ADARB2_p1779233 ADARB2 442.43/229.1 395.57/651.14 335.76 523.36 0.63881 27707 14238 1.5721 0.9419 0.058104 0.11621 0.33552 True -AFF3_m100625319 AFF3 742.28/781.42 955.8/996.17 761.85 975.98 0.35693 790.26 18669 1.5672 0.94146 0.058537 0.11707 0.33705 True -ADPRHL2_m36554576 ADPRHL2 1099.5/1115.3 1309.1/1431.6 1107.4 1370.3 0.30714 3814.3 28306 1.563 0.94098 0.059024 0.11805 0.33855 True -ADCK3_m227149096 ADCK3 270.46/415.57 434.05/525.83 343.01 479.94 0.4834 7370.6 7684 1.5621 0.94086 0.059137 0.11827 0.33855 True -ACVR2B_p38495805 ACVR2B 804.02/776.09 550.14/595.07 790.05 572.6 -0.46373 699.76 19440 1.5596 0.059426 0.94057 0.11885 0.33924 False -ACSS2_p33501184 ACSS2 698.19/531.01 504.08/347.89 614.6 425.98 -0.52781 13087 14701 1.5556 0.059898 0.9401 0.1198 0.34096 False -AAAS_m53715169 AAAS 956.89/1113.5 502.82/966.99 1035.2 734.9 -0.49372 59996 37506 1.5506 0.060496 0.9395 0.12099 0.34299 False -AATK_p79104850 AATK 438.02/561.2 319.86/344.45 499.61 332.16 -0.58749 3944.4 11675 1.5498 0.060597 0.9394 0.12119 0.34299 False -ABCC1_m16043645 ABCC1 1609.5/978.55 807.54/1025.9 1294 916.73 -0.49684 1.1145e+05 59593 1.5456 0.061105 0.9389 0.12221 0.34488 False -ACTL6A_m179287986 ACTL6A 661.44/358.74 296.52/337.59 510.09 317.05 -0.68432 23329 15741 1.5388 0.061929 0.93807 0.12386 0.3477 False -ADRA1B_p159343912 ADRA1B 1149.4/1465.2 960.21/1086.6 1307.3 1023.4 -0.35292 28911 34048 1.5386 0.061952 0.93805 0.1239 0.3477 False -AGAP3_p150784015 AGAP3 486.53/673.08 784.83/734.68 579.81 759.75 0.38937 9329.5 13778 1.533 0.93737 0.062632 0.12526 0.34996 True -ACTR3_p114684915 ACTR3 895.15/824.04 700.29/571.04 859.6 635.66 -0.43481 5440.7 21353 1.5325 0.062705 0.93729 0.12541 0.34996 False -ADAM10_m59009777 ADAM10 539.44/1021.2 1090.8/1021.3 780.31 1056.1 0.43612 59222 32523 1.5292 0.93689 0.063113 0.12623 0.35125 True -ACAT2_p160183152 ACAT2 2185.7/1877.2 2122.9/2834.6 2031.4 2478.8 0.28699 1.5041e+05 87211 1.5148 0.93508 0.064918 0.12984 0.36029 True -ABL2_m179100447 ABL2 1425.8/1026.5 1555.1/1463.6 1226.1 1509.4 0.29963 41949 35119 1.5115 0.93467 0.065331 0.13066 0.36158 True -ACVR1C_m158443700 ACVR1C 992.16/637.57 876.94/1399.6 814.86 1138.2 0.48168 99717 46653 1.4972 0.93282 0.067177 0.13435 0.37018 True -ACAT2_m160183968 ACAT2 1816.8/2846.8 1813.8/1763.5 2331.8 1788.6 -0.3824 2.659e+05 1.3186e+05 1.4958 0.067352 0.93265 0.1347 0.37018 False -ACIN1_m23538815 ACIN1 1308.2/2186.2 1482.6/997.31 1747.2 1240 -0.49441 2.516e+05 1.1521e+05 1.4944 0.06754 0.93246 0.13508 0.37018 False -AGPAT3_p45379552 AGPAT3 1043.6/1156.1 1405.6/1294.8 1099.9 1350.2 0.29563 6233.8 28093 1.4937 0.93237 0.067625 0.13525 0.37018 True -ABTB1_m127395233 ABTB1 598.24/546.99 482.63/314.7 572.61 398.67 -0.52129 7706.7 13588 1.4923 0.067814 0.93219 0.13563 0.3702 False -ACLY_m40070029 ACLY 132.29/241.53 123.02/65.801 186.91 94.412 -0.97779 3802 3911.5 1.4888 0.068272 0.93173 0.13654 0.37168 False -ADARB1_m46591572 ADARB1 205.78/186.47 225.86/359.9 196.13 292.88 0.57609 4585.1 4279.5 1.479 0.93033 0.069666 0.13933 0.37824 True -AHCTF1_m247068945 AHCTF1 677.61/316.12 613.86/819.36 496.86 716.61 0.52745 43227 22145 1.4767 0.93009 0.069912 0.13982 0.37855 True -AFF2_m147924923 AFF2 601.18/328.55 370.96/181.95 464.86 276.46 -0.74764 27513 16354 1.4742 0.070213 0.92979 0.14043 0.37915 False -ADCK1_p78285402 ADCK1 1246.5/753 1301.5/1264.5 999.73 1283 0.35962 61215 37246 1.4679 0.92894 0.071062 0.14212 0.3827 True -ADIRF_p88729942 ADIRF 951.01/864.89 606.92/769.01 907.95 687.96 -0.39977 8422.9 22694 1.4603 0.072108 0.92789 0.14422 0.38729 False -ACVR1B_p52369243 ACVR1B 692.31/749.45 607.55/448.02 720.88 527.78 -0.44908 7178.6 17556 1.4574 0.072508 0.92749 0.14502 0.38839 False -ACAD9_m128598631 ACAD9 399.8/609.15 281.38/399.95 504.48 340.67 -0.56506 14472 12692 1.4541 0.072959 0.92704 0.14592 0.38977 False -ADRA1A_p26722438 ADRA1A 1093.6/934.15 726.79/835.96 1013.9 781.37 -0.37536 9334.6 25659 1.4514 0.07333 0.92667 0.14666 0.3907 False -AFTPH_p64778713 AFTPH 1731.5/1507.8 1543.8/999.6 1619.6 1271.7 -0.34868 86548 57661 1.449 0.073666 0.92633 0.14733 0.39145 False -ADCK2_m140373155 ADCK2 77.903/97.677 99.681/212.28 87.79 155.98 0.82211 3267.4 2214.8 1.4489 0.92396 0.076041 0.15208 0.39793 True -ADI1_m3523162 ADI1 592.36/710.38 362.76/578.48 651.37 470.62 -0.46807 15115 15683 1.4433 0.074462 0.92554 0.14892 0.39463 False -ADCY1_p45614127 ADCY1 1725.6/2271.4 1786.7/1442.5 1998.5 1614.6 -0.30762 1.041e+05 71107 1.4399 0.074949 0.92505 0.1499 0.39616 False -AFMID_p76187082 AFMID 1137.7/1028.3 803.75/885.74 1083 844.75 -0.35804 4672.8 27613 1.4337 0.075835 0.92417 0.15167 0.39793 False -AFF1_m87967432 AFF1 173.44/602.05 147.63/215.14 387.75 181.38 -1.0918 47064 21559 1.4316 0.076124 0.92388 0.15225 0.39793 False -ABHD14B_p52004011 ABHD14B 501.23/635.79 668.74/800.48 568.51 734.61 0.36923 8865.6 13480 1.4307 0.92374 0.07626 0.15252 0.39793 True -ABT1_p26597361 ABT1 339.54/447.54 310.4/205.99 393.54 258.19 -0.60615 5641.4 8952.9 1.4305 0.07628 0.92372 0.15256 0.39793 False -ACAD11_p132378566 ACAD11 679.08/777.87 449.82/627.11 728.47 538.47 -0.43532 10297 17761 1.4257 0.076978 0.92302 0.15396 0.40053 False -ACTR5_m37377189 ACTR5 679.08/774.31 1578.5/680.32 726.7 1129.4 0.63543 2.0394e+05 79789 1.4257 0.92263 0.077373 0.15475 0.40069 True -ADARB2_m1779337 ADARB2 665.85/701.5 986.71/746.7 683.68 866.7 0.34178 14719 16550 1.4227 0.92259 0.077411 0.15482 0.40069 True -ACVR1C_m158401030 ACVR1C 1099.5/1435 1341.3/1834.4 1267.2 1587.8 0.32518 88938 51572 1.4119 0.92101 0.078993 0.15799 0.40783 True -ADH7_p100349698 ADH7 1087.7/847.13 994.28/1551.8 967.42 1273 0.39569 92164 46957 1.4103 0.92077 0.079228 0.15846 0.40798 True -ADRA1B_p159343905 ADRA1B 410.09/293.03 44.162/334.73 351.56 189.44 -0.88851 24533 13442 1.4058 0.079898 0.9201 0.1598 0.41038 False -ADARB2_p1421336 ADARB2 561.49/543.44 881.98/580.76 552.47 731.37 0.40409 22765 16293 1.4016 0.91949 0.080514 0.16103 0.412 True -ABTB1_p127395220 ABTB1 652.62/1113.5 814.48/296.39 883.07 555.43 -0.66795 1.2021e+05 54739 1.4009 0.080627 0.91937 0.16125 0.412 False -ADRA1B_m159343926 ADRA1B 526.21/820.49 1157.1/708.36 673.35 932.71 0.46947 71980 34842 1.3895 0.91764 0.08236 0.16472 0.41884 True -AEBP1_p44144306 AEBP1 971.58/944.8 922.99/509.81 958.19 716.4 -0.41904 42858 30350 1.3879 0.082582 0.91742 0.16516 0.41884 False -ADCK1_m78285358 ADCK1 1547.8/1966 1870/2354 1756.9 2112 0.26543 1.0229e+05 65637 1.386 0.91712 0.082878 0.16576 0.41884 True -AATK_m79102275 AATK 912.79/772.54 2881.9/634.55 842.66 1758.2 1.0602 1.2676e+06 4.3645e+05 1.3859 0.90779 0.092212 0.18442 0.44502 True -ABI1_p27112174 ABI1 1484.6/1314.2 2124.2/1406.4 1399.4 1765.3 0.33492 1.3606e+05 69839 1.3847 0.91692 0.083076 0.16615 0.41884 True -ACO2_p41895716 ACO2 302.79/348.09 404.4/481.78 325.44 443.09 0.44403 2009.6 7247.4 1.382 0.9165 0.083496 0.16699 0.41884 True -ACVR1C_m158401075 ACVR1C 283.69/312.57 297.15/608.23 298.13 452.69 0.60095 24401 12516 1.3815 0.91612 0.083879 0.16776 0.41884 True -AEBP2_m19615578 AEBP2 348.36/671.31 820.79/597.36 509.83 709.07 0.47511 38554 20812 1.3811 0.91635 0.083646 0.16729 0.41884 True -AGFG1_m228337201 AGFG1 933.37/880.87 635.31/763.29 907.12 699.3 -0.37491 4784 22671 1.3802 0.083756 0.91624 0.16751 0.41884 False -ACTL7A_p111624607 ACTL7A 346.89/715.71 558.34/1060.8 531.3 809.58 0.60672 97130 40711 1.3792 0.91573 0.084272 0.16854 0.41884 True -AFF4_p132272853 AFF4 756.98/676.64 883.25/914.35 716.81 898.8 0.326 1855.7 17445 1.3778 0.91587 0.084127 0.16825 0.41884 True -ACIN1_m23535193 ACIN1 561.49/824.04 428.37/579.05 692.77 503.71 -0.45899 22909 18833 1.3776 0.084162 0.91584 0.16832 0.41884 False -ADARB2_m1779308 ADARB2 1140.6/1191.7 880.09/977.86 1166.1 928.97 -0.32771 3040.9 29983 1.3697 0.085394 0.91461 0.17079 0.42337 False -ADCK5_m145603137 ADCK5 1195/1083.3 659.91/1098 1139.2 878.96 -0.37373 51102 36508 1.3618 0.086629 0.91337 0.17326 0.42843 False -ADH5_m100003254 ADH5 1178.8/1243.2 868.1/1075.1 1211 971.62 -0.31745 11749 31269 1.3537 0.087909 0.91209 0.17582 0.43368 False -ADARB2_p1421307 ADARB2 418.91/300.14 376.64/645.42 359.52 511.03 0.50613 21588 12593 1.3501 0.91144 0.088556 0.17711 0.4358 True -AEN_m89169544 AEN 536.5/687.29 504.71/394.23 611.9 449.47 -0.44421 8735.7 14629 1.3429 0.089651 0.91035 0.1793 0.43999 False -ACIN1_p23538745 ACIN1 568.84/612.7 564.02/270.64 590.77 417.33 -0.5004 21998 16711 1.3417 0.089848 0.91015 0.1797 0.43999 False -ADCK2_m140373220 ADCK2 345.42/582.51 849.18/484.07 463.97 666.62 0.52191 47380 22961 1.3374 0.90935 0.090646 0.18129 0.44173 True -AHCY_m32883301 AHCY 392.46/412.02 334.37/214 402.24 274.18 -0.55124 3718.3 9173.3 1.3371 0.090599 0.9094 0.1812 0.44173 False -ADI1_m3517677 ADI1 414.5/488.39 226.49/403.96 451.44 315.22 -0.51679 9238.6 10430 1.3339 0.091125 0.90887 0.18225 0.44261 False -ACTL7A_p111624699 ACTL7A 1214.1/776.09 893.97/543.57 995.1 718.77 -0.46875 78661 42974 1.333 0.091269 0.90873 0.18254 0.44261 False -ADRBK1_p67034261 ADRBK1 680.55/634.01 318.6/631.12 657.28 474.86 -0.46818 24958 18880 1.3276 0.092148 0.90785 0.1843 0.44502 False -ADAM10_m59009883 ADAM10 1197.9/1882.5 1693.9/2150.3 1540.2 1922.1 0.31936 1.6921e+05 83648 1.3204 0.90664 0.093357 0.18671 0.44946 True -ADNP_m49518579 ADNP 1805/1435 1712.2/2152.5 1620 1932.4 0.25426 82700 56385 1.3156 0.90585 0.094147 0.18829 0.45218 True -AGL_m100327186 AGL 138.17/159.84 400.62/132.75 149 266.68 0.83553 18056 8045.4 1.312 0.90042 0.099578 0.19916 0.46422 True -AGL_p100327074 AGL 607.06/985.65 651.08/514.39 796.35 582.73 -0.44991 40504 26576 1.3104 0.095036 0.90496 0.19007 0.45433 False -AFF4_p132272762 AFF4 561.49/474.18 718.58/605.94 517.83 662.26 0.3543 5078 12150 1.3103 0.90495 0.09505 0.1901 0.45433 True -ACP1_p264954 ACP1 1267/1397.7 929.3/1247.4 1332.3 1088.3 -0.29162 29557 34775 1.3086 0.095342 0.90466 0.19068 0.45464 False -ABL1_m133729532 ABL1 1400.8/1465.2 2174.7/1405.3 1433 1790 0.32073 1.4903e+05 74817 1.3052 0.90409 0.095914 0.19183 0.45628 True -ADRM1_p60878642 ADRM1 94.072/124.32 70.66/451.45 109.19 261.06 1.2498 36479 13594 1.3025 0.88325 0.11675 0.2335 0.50165 True -AEBP2_p19615498 AEBP2 936.31/907.51 624.58/824.51 921.91 724.55 -0.34712 10201 23083 1.299 0.096966 0.90303 0.19393 0.45921 False -AFMID_p76183450 AFMID 798.14/1559.3 606.92/1007.6 1178.7 807.26 -0.54554 1.8497e+05 81886 1.2982 0.097118 0.90288 0.19424 0.45921 False -AFF2_m147891420 AFF2 505.64/122.54 115.45/183.1 314.09 149.28 -1.0681 37834 17256 1.2976 0.09722 0.90278 0.19444 0.45921 False -AGPAT5_m6566307 AGPAT5 288.09/250.41 232.8/107.57 269.25 170.18 -0.65875 4275.6 5869.9 1.2942 0.097799 0.9022 0.1956 0.46018 False -ABLIM2_m8160405 ABLIM2 1336.1/1784.8 1398.7/1144.9 1560.5 1271.8 -0.29489 66433 49787 1.2937 0.097886 0.90211 0.19577 0.46018 False -ABL1_m133729488 ABL1 621.76/651.77 688.3/35.475 636.76 361.89 -0.8135 1.0677e+05 45785 1.2922 0.098144 0.90186 0.19629 0.46031 False -ACVRL1_m52306921 ACVRL1 740.81/525.68 1467.4/548.15 633.25 1007.8 0.66952 2.2285e+05 84415 1.2891 0.89986 0.10014 0.20028 0.46422 True -ADCK4_p41220422 ADCK4 161.69/333.88 275.07/465.18 247.78 370.13 0.57703 16449 9050.7 1.286 0.90032 0.09968 0.19936 0.46422 True -AHNAK2_m105423809 AHNAK2 404.21/470.63 489.57/643.13 437.42 566.35 0.37193 6997.9 10070 1.2848 0.90057 0.099429 0.19886 0.46422 True -AATK_p79102286 AATK 1887.3/998.08 1163.4/929.22 1442.7 1046.3 -0.46311 2.1139e+05 95793 1.2808 0.10013 0.89987 0.20027 0.46422 False -ADIRF_p88729951 ADIRF 435.08/435.11 275.7/339.88 435.09 307.79 -0.49803 1029.7 10011 1.2724 0.10161 0.89839 0.20322 0.46994 False -ADAR_m154574219 ADAR 634.98/1269.8 1618.9/1005.9 952.39 1312.4 0.46214 1.9468e+05 80849 1.266 0.89721 0.10279 0.20558 0.47431 True -ADH5_p100002504 ADH5 668.79/717.48 772.84/938.95 693.14 855.89 0.3039 7490.8 16805 1.2555 0.89535 0.10465 0.2093 0.48177 True -AHNAK2_m105444541 AHNAK2 761.39/664.2 846.65/909.2 712.8 877.93 0.30022 3339.3 17337 1.2541 0.8951 0.1049 0.20981 0.48183 True -ABCB8_p150730680 ABCB8 773.15/854.23 638.46/633.98 813.69 636.22 -0.35447 1648.5 20088 1.2522 0.10525 0.89475 0.21051 0.48234 False -ACVR1B_m52369192 ACVR1B 632.04/475.95 1491.4/427.99 554 959.71 0.79161 2.8881e+05 1.05e+05 1.252 0.88991 0.11009 0.22017 0.49176 True -ADRB3_m37823867 ADRB3 251.35/237.98 251.09/438.86 244.66 344.98 0.49401 8859 6470.9 1.2471 0.89369 0.10631 0.21263 0.48496 True -ACTL6A_p179287911 ACTL6A 1281.7/1189.9 1228.3/725.53 1235.8 976.93 -0.33881 65315 43093 1.247 0.10619 0.89381 0.21238 0.48496 False -AFF2_m147924918 AFF2 858.4/1136.6 795.55/2439.8 997.51 1617.7 0.69696 6.9522e+05 2.4854e+05 1.244 0.89076 0.10924 0.21847 0.49176 True -ADIRF_m88728350 ADIRF 244/239.75 130.59/174.52 241.88 152.55 -0.66147 486.77 5210.1 1.2394 0.1076 0.8924 0.2152 0.48865 False -ADIRF_m88728302 ADIRF 492.41/221.99 309.77/102.99 357.2 206.38 -0.78849 28970 15015 1.2393 0.10761 0.89239 0.21522 0.48865 False -ACTL6A_p179287902 ACTL6A 754.04/884.42 623.32/663.16 819.23 643.24 -0.34844 4646.4 20240 1.2371 0.10803 0.89197 0.21607 0.48946 False -AFF4_p132272771 AFF4 551.2/1387 1147/1512.3 969.11 1329.6 0.45588 2.0801e+05 85604 1.2322 0.891 0.109 0.21799 0.49176 True -ADH5_m100006354 ADH5 1427.2/1157.9 1642.8/1393.3 1292.6 1518 0.2318 33706 33650 1.2291 0.89048 0.10952 0.21903 0.49176 True -AHNAK_m62303528 AHNAK 266.05/797.4 677.58/839.39 531.72 758.48 0.51163 77130 34052 1.2288 0.89022 0.10978 0.21957 0.49176 True -AGAP3_p150783972 AGAP3 366/369.4 333.74/746.7 367.7 540.22 0.55377 42636 19746 1.2277 0.88973 0.11027 0.22053 0.49176 True -AFAP1L2_p116092995 AFAP1L2 1383.1/864.89 894.6/851.98 1124 873.29 -0.36376 67603 41721 1.2275 0.10982 0.89018 0.21963 0.49176 False -ADIRF_m88729968 ADIRF 734.94/673.08 620.8/470.91 704.01 545.85 -0.3665 6573.2 17099 1.2095 0.11323 0.88677 0.22647 0.50165 False -ACSL6_p131329816 ACSL6 1797.7/1074.4 1233.4/983.58 1436.1 1108.5 -0.37322 1.4636e+05 73986 1.2043 0.11424 0.88576 0.22849 0.50165 False -ABTB1_m127395855 ABTB1 560.02/887.97 1113.5/759.28 724 936.4 0.37069 58259 31180 1.2029 0.88549 0.11451 0.22902 0.50165 True -AHCTF1_p247076572 AHCTF1 551.2/740.57 1103.4/622.53 645.89 862.98 0.41749 66780 32617 1.2021 0.88531 0.11469 0.22938 0.50165 True -ADRM1_m60878651 ADRM1 736.41/662.43 562.12/523.55 699.42 542.83 -0.36505 1740.2 16975 1.2018 0.11472 0.88528 0.22943 0.50165 False -AHRR_m344069 AHRR 415.97/372.95 212.61/349.03 394.46 280.82 -0.48876 5115.4 8976.2 1.1995 0.11516 0.88484 0.23032 0.50165 False -ACVR1B_p52369255 ACVR1B 167.57/166.94 44.162/150.48 167.25 97.323 -0.77502 2826.2 3457 1.1994 0.11518 0.88482 0.23037 0.50165 False -ADD3_p111860553 ADD3 1881.4/1378.1 1584.8/1004.2 1629.8 1294.5 -0.33207 1.4761e+05 78215 1.1989 0.11528 0.88472 0.23056 0.50165 False -ADCK3_p227149133 ADCK3 724.65/648.22 702.81/999.6 686.43 851.21 0.30998 23481 18910 1.1982 0.88458 0.11542 0.23083 0.50165 True -AGPAT5_p6566257 AGPAT5 1383.1/1310.6 1145.1/1099.2 1346.9 1122.1 -0.26321 1840.8 35198 1.1981 0.11543 0.88457 0.23086 0.50165 False -ADPRHL2_p36554501 ADPRHL2 598.24/694.4 675.68/921.21 646.32 798.45 0.30453 17383 16159 1.1968 0.8843 0.1157 0.2314 0.50165 True -ACRC_m70811980 ACRC 461.54/502.59 445.41/889.17 482.07 667.29 0.46825 49652 24031 1.1948 0.88382 0.11618 0.23237 0.50165 True -AGAP3_p150783903 AGAP3 693.78/678.41 784.2/895.46 686.1 839.83 0.2913 3154.2 16616 1.1926 0.8835 0.1165 0.23301 0.50165 True -AHCTF1_m247094617 AHCTF1 845.18/660.65 541.3/640.84 752.91 591.07 -0.34862 10989 18426 1.1923 0.11658 0.88342 0.23315 0.50165 False -AAAS_m53715212 AAAS 373.35/603.82 270.65/424.56 488.58 347.61 -0.48996 19201 13993 1.1919 0.11666 0.88334 0.23331 0.50165 False -ACTRT3_m169487192 ACTRT3 510.05/172.27 218.29/177.38 341.16 197.83 -0.7831 28942 14739 1.1916 0.1167 0.8833 0.23341 0.50165 False -ACD_m67694260 ACD 1189.1/1653.4 1320.5/977.29 1421.3 1148.9 -0.30672 83330 52689 1.1867 0.11767 0.88233 0.23534 0.50452 False -ADAMTS5_p28338620 ADAMTS5 399.8/209.56 357.08/470.33 304.68 413.71 0.44006 12254 8574.9 1.1774 0.88042 0.11958 0.23917 0.51163 True -ACVR2B_p38518810 ACVR2B 1146.5/1292.9 1507.8/1346.9 1219.7 1427.4 0.22667 11831 31519 1.1698 0.87895 0.12105 0.24209 0.51589 True -AEN_m89169478 AEN 1381.7/1330.2 1509.1/327.29 1355.9 918.19 -0.56192 3.4983e+05 1.4025e+05 1.1695 0.1211 0.8789 0.24219 0.51589 False -ADRM1_m60878634 ADRM1 410.09/657.1 435.31/356.47 533.6 395.89 -0.42971 16807 13977 1.1648 0.12205 0.87795 0.2441 0.51884 False -ABL2_p179100503 ABL2 951.01/994.53 996.17/504.09 972.77 750.13 -0.37451 61010 36673 1.1626 0.1225 0.8775 0.245 0.51965 False -ADNP2_p77875482 ADNP2 561.49/390.71 480.11/775.31 476.1 627.71 0.3981 29077 17069 1.1604 0.87704 0.12296 0.24592 0.51977 True -ACHE_m100491735 ACHE 1519.8/1601.9 1329.3/1320.6 1560.9 1324.9 -0.23626 1702.3 41476 1.1585 0.12333 0.87667 0.24666 0.51977 False -ACVR1B_p52369266 ACVR1B 1170/1205.9 1523/1258.2 1187.9 1390.6 0.22706 17843 30607 1.1584 0.87664 0.12336 0.24672 0.51977 True -AFF4_m132272826 AFF4 1155.3/2191.5 2025.8/2123.4 1673.4 2074.6 0.30985 2.7081e+05 1.2015e+05 1.1573 0.87643 0.12357 0.24714 0.51977 True -AEBP2_m19615484 AEBP2 104.36/387.16 322.38/409.68 245.76 366.03 0.57281 21899 10835 1.1555 0.87491 0.12509 0.25018 0.52376 True -AFF1_p87967353 AFF1 1785.9/1459.8 960.21/1647.3 1622.9 1303.8 -0.31564 1.446e+05 77077 1.1494 0.1252 0.8748 0.2504 0.52376 False -ADH7_p100350718 ADH7 171.97/321.45 229.01/65.229 246.71 147.12 -0.74188 12292 7648 1.1489 0.1253 0.8747 0.25061 0.52376 False -AEN_m89169484 AEN 1327.3/903.96 975.99/821.65 1115.6 898.82 -0.31144 50758 35946 1.1435 0.12641 0.87359 0.25282 0.52704 False -ADNP_m49511043 ADNP 408.62/523.9 483.89/701.49 466.26 592.69 0.34548 15160 12261 1.1418 0.87323 0.12677 0.25355 0.52704 True -ACSL6_p131329859 ACSL6 242.53/225.55 324.91/304.97 234.04 314.94 0.42676 171.47 5022.7 1.1416 0.87312 0.12688 0.25376 0.52704 True -A1CF_p52595870 A1CF 915.73/1035.4 948.23/639.13 975.55 793.68 -0.29733 27465 25543 1.138 0.12756 0.87244 0.25512 0.52877 False -AFF3_m100625326 AFF3 467.42/451.09 520.48/629.97 459.25 575.23 0.32421 3063.5 10631 1.1248 0.86966 0.13034 0.26068 0.53916 True -AGBL5_p27275870 AGBL5 684.96/546.99 394.31/566.46 615.98 480.38 -0.35803 12168 14737 1.1169 0.13201 0.86799 0.26402 0.54481 False -AGPAT3_p45379618 AGPAT3 58.795/703.28 440.99/837.1 381.04 639.05 0.74447 1.4306e+05 53446 1.116 0.86089 0.13911 0.27822 0.56377 True -ACTL6B_m100253081 ACTL6B 582.07/690.84 473.17/524.12 636.46 498.64 -0.35143 3607 15284 1.1148 0.13248 0.86752 0.26496 0.54481 False -ABL1_p133729502 ABL1 774.62/529.23 675.68/228.87 651.93 452.28 -0.52653 64964 32120 1.1145 0.13252 0.86748 0.26504 0.54481 False -AHRR_m353892 AHRR 351.3/150.96 288.95/416.55 251.13 352.75 0.48857 14105 8323.1 1.1139 0.86694 0.13306 0.26612 0.5459 True -ADAMTS5_m28338697 ADAMTS5 1068.6/749.45 541.93/875.44 909.02 708.69 -0.35872 53270 32906 1.1044 0.13471 0.86529 0.26942 0.55154 False -AGBL5_p27275898 AGBL5 1108.3/1969.5 1066.2/1333.8 1538.9 1200 -0.35863 2.0333e+05 94995 1.0997 0.13574 0.86426 0.27148 0.55463 False -ACVR2B_p38518836 ACVR2B 1465.5/777.87 744.45/951.54 1121.7 847.99 -0.4031 1.2892e+05 62115 1.0981 0.13608 0.86392 0.27217 0.55489 False -ACTR1A_p104262351 ACTR1A 973.05/1099.3 768.42/2344.2 1036.2 1556.3 0.5864 6.2477e+05 2.2578e+05 1.0947 0.86114 0.13886 0.27772 0.56377 True -AGTPBP1_p88307700 AGTPBP1 157.28/118.99 40.377/603.08 138.13 321.73 1.2139 79525 28371 1.09 0.82636 0.17364 0.34729 0.6308 True -ADRB1_p115803878 ADRB1 438.02/1149 1278.8/877.15 793.53 1078 0.4415 1.6672e+05 68596 1.0861 0.86111 0.13889 0.27778 0.56377 True -ABL1_p133729441 ABL1 630.57/417.35 495.25/280.94 523.96 388.09 -0.43209 22848 15823 1.0802 0.14003 0.85997 0.28006 0.56546 False -AFF2_m147891412 AFF2 774.62/500.82 838.45/723.24 637.72 780.84 0.2917 22061 17565 1.0799 0.85991 0.14009 0.28018 0.56546 True -ADIRF_p88729984 ADIRF 1328.8/1571.7 1123/1356.6 1450.2 1239.8 -0.226 28405 38217 1.0764 0.14088 0.85912 0.28175 0.56748 False -ACAD9_m128598607 ACAD9 133.76/214.89 205.67/272.36 174.32 239.01 0.4531 2757.4 3619.8 1.0752 0.85859 0.14141 0.28281 0.56847 True -ACHE_p100491631 ACHE 540.91/587.84 672.53/703.78 564.38 688.16 0.28562 794.73 13371 1.0705 0.8578 0.1422 0.28441 0.57053 True -ADAR_m154574098 ADAR 1572.8/1028.3 1218.2/871.43 1300.5 1044.8 -0.31553 1.0419e+05 57297 1.0681 0.14273 0.85727 0.28546 0.57108 False -ACVR2A_p148602754 ACVR2A 514.45/252.18 461.18/539.57 383.32 500.37 0.38358 18733 12041 1.0667 0.85692 0.14308 0.28615 0.57108 True -AFTPH_m64778841 AFTPH 746.69/456.42 1070/553.3 601.56 811.64 0.43153 87807 38838 1.066 0.85663 0.14337 0.28674 0.57108 True -AHNAK2_m105423822 AHNAK2 316.02/735.24 709.75/664.87 525.63 687.31 0.38627 44440 23049 1.065 0.85651 0.14349 0.28697 0.57108 True -AGBL5_p27275854 AGBL5 296.91/829.37 489.57/1233.1 563.14 861.31 0.61215 2.0907e+05 78581 1.0637 0.85298 0.14702 0.29403 0.57688 True -ADAM12_m128019019 ADAM12 1106.8/1305.3 1081.3/955.54 1206.1 1018.4 -0.24372 13808 31127 1.0634 0.14379 0.85621 0.28758 0.57116 False -AGPAT5_m6566240 AGPAT5 802.55/1594.8 872.52/950.39 1198.7 911.46 -0.39482 1.5843e+05 73420 1.06 0.14457 0.85543 0.28914 0.5713 False -ABTB1_p127395242 ABTB1 842.24/1424.3 853.59/946.96 1133.3 900.28 -0.33173 86882 48323 1.0599 0.14459 0.85541 0.28918 0.5713 False -ABT1_p26597293 ABT1 523.27/694.4 336.9/597.93 608.83 467.41 -0.38064 24355 17817 1.0595 0.14468 0.85532 0.28936 0.5713 False -ACTR8_m53916073 ACTR8 1500.7/1316 800.6/1476.2 1408.4 1138.4 -0.30675 1.2265e+05 65545 1.0544 0.14585 0.85415 0.2917 0.57477 False -AGBL5_m27276001 AGBL5 624.7/554.1 370.96/558.45 589.4 464.71 -0.34226 10034 14032 1.0526 0.14626 0.85374 0.29251 0.57523 False -AEBP2_p19615557 AEBP2 1187.7/859.56 1404.4/1045.9 1023.6 1225.2 0.25907 59027 36965 1.0483 0.85275 0.14725 0.2945 0.57688 True -ADRBK1_m67034255 ADRBK1 586.48/987.43 743.82/1312 786.95 1027.9 0.38494 1.209e+05 53204 1.0447 0.85186 0.14814 0.29627 0.57921 True -AFMID_m76183493 AFMID 446.84/191.8 220.18/208.85 319.32 214.51 -0.57174 16293 10162 1.0425 0.14858 0.85142 0.29717 0.57983 False -ADAR_p154574121 ADAR 0/8.8797 0/0 4.4399 0 -2.4436 19.712 61.451 1.0394 0.85069 0.14931 0.29862 0.5806 False -ADCK4_p41220276 ADCK4 680.55/534.56 550.77/1025.9 607.56 788.34 0.37527 61771 30266 1.0392 0.85061 0.14939 0.29879 0.5806 True -AATF_p35307505 AATF 995.1/477.73 728.05/275.22 736.42 501.63 -0.55297 1.1818e+05 51379 1.0379 0.14966 0.85034 0.29931 0.5806 False -ADRB2_p148206432 ADRB2 467.42/1118.8 592.41/537.28 793.13 564.84 -0.48898 1.0685e+05 48633 1.0358 0.15015 0.84985 0.3003 0.5814 False -AGFG1_m228337138 AGFG1 1049.5/1001.6 1732.4/890.89 1025.6 1311.7 0.35467 1.7762e+05 76532 1.0342 0.84945 0.15055 0.30109 0.58142 True -ABL2_m179100597 ABL2 621.76/765.43 919.84/735.25 693.59 827.55 0.25441 13679 16818 1.0329 0.84918 0.15082 0.30165 0.58142 True -ACTR5_p37377198 ACTR5 373.35/479.51 424.59/652.29 426.43 538.44 0.33578 15779 11786 1.0318 0.8489 0.1511 0.30219 0.58142 True -ACTR5_p37377178 ACTR5 379.23/664.2 864.32/508.1 521.72 686.21 0.39472 52027 25510 1.0299 0.84839 0.15161 0.30322 0.58142 True -ADRBK1_m67034242 ADRBK1 1462.5/1209.4 1537.5/1519.1 1336 1528.3 0.19391 16099 34881 1.0299 0.84846 0.15154 0.30308 0.58142 True -ACAD11_m132378487 ACAD11 546.79/717.48 826.47/690.62 632.14 758.54 0.26262 11897 15168 1.0264 0.84764 0.15236 0.30472 0.58317 True -AFF1_m87967920 AFF1 751.1/1630.3 1093.3/632.83 1190.7 863.08 -0.46379 2.4627e+05 1.0255e+05 1.0235 0.15304 0.84696 0.30609 0.58416 False -AHCY_p32883227 AHCY 1071.5/406.69 645.4/349.6 739.11 497.5 -0.57015 1.3238e+05 56160 1.0228 0.1532 0.8468 0.30641 0.58416 False -ABTB1_m127395403 ABTB1 846.65/1557.5 482.63/1248.5 1202.1 865.57 -0.47335 2.7297e+05 1.1166e+05 1.0076 0.15683 0.84317 0.31365 0.59683 False -ACAT2_p160183940 ACAT2 22.048/0 25.866/22.315 11.024 24.091 1.0612 124.68 168.55 1.0065 0.80415 0.19585 0.3917 0.661 True -ADCK4_m41220465 ADCK4 812.84/845.35 946.33/999.6 829.09 972.97 0.2306 973.58 20512 1.0046 0.84245 0.15755 0.31511 0.59815 True -ACP1_p272032 ACP1 1616.9/1427.9 1369/1272.5 1522.4 1320.8 -0.20477 11258 40338 1.0037 0.15777 0.84223 0.31554 0.59815 False -ABHD14B_p52004005 ABHD14B 186.67/415.57 333.74/472.62 301.12 403.18 0.41987 17921 10405 1.0005 0.84122 0.15878 0.31756 0.60085 True -ADAM10_m59041696 ADAM10 499.76/458.19 409.45/338.73 478.98 374.09 -0.35573 1682 11140 0.99376 0.16017 0.83983 0.32034 0.6044 False -ADRM1_p60878730 ADRM1 621.76/635.79 392.41/621.39 628.77 506.9 -0.31028 13157 15078 0.99248 0.16048 0.83952 0.32096 0.6044 False -ACVR2B_m38518849 ACVR2B 646.74/737.02 715.43/378.78 691.88 547.11 -0.33815 30370 21304 0.99188 0.16063 0.83937 0.32126 0.6044 False -ACSL6_m131326630 ACSL6 1031.8/605.6 802.49/1259.4 818.72 1030.9 0.33214 97607 46020 0.98921 0.83871 0.16129 0.32258 0.60575 True -AES_m3056337 AES 827.54/1225.4 1143.2/1261.1 1026.5 1202.1 0.2277 43050 31693 0.98671 0.83811 0.16189 0.32378 0.60652 True -ACVRL1_m52306910 ACVRL1 1275.8/1136.6 1464.9/1295.4 1206.2 1380.2 0.1942 12030 31132 0.98585 0.8379 0.1621 0.32421 0.60652 True -ADCK4_m41220291 ADCK4 224.89/188.25 700.29/81.25 206.57 390.77 0.9164 96138 34960 0.98514 0.81248 0.18752 0.37505 0.64631 True -AAAS_p53714374 AAAS 430.67/463.52 540.04/553.87 447.1 546.96 0.29025 317.6 10318 0.98307 0.83721 0.16279 0.32557 0.60794 True -ACTN4_p39138433 ACTN4 867.22/840.02 1026.5/966.41 853.62 996.44 0.22294 1086.3 21188 0.98111 0.83673 0.16327 0.32654 0.6086 True -ACVR2A_p148653965 ACVR2A 573.25/168.72 119.87/333.58 370.98 226.73 -0.70794 52330 23033 0.97466 0.16486 0.83514 0.32973 0.61302 False -ACTL6B_p100253446 ACTL6B 1209.7/827.59 1085.1/472.62 1018.6 778.88 -0.38675 1.3029e+05 60627 0.97385 0.16507 0.83493 0.33013 0.61302 False -AGPAT3_m45379590 AGPAT3 1203.8/1630.3 1392.4/997.88 1417.1 1195.1 -0.24556 84380 52957 0.96445 0.16741 0.83259 0.33482 0.62057 False -ABLIM2_p8108320 ABLIM2 149.93/67.486 97.788/234.59 108.71 166.19 0.60785 6378.1 3553.6 0.96432 0.82664 0.17336 0.34671 0.6308 True -ABCB8_m150730720 ABCB8 146.99/266.39 292.73/247.75 206.69 270.24 0.38516 4070.2 4374.5 0.9609 0.83155 0.16845 0.3369 0.62326 True -ACVR2A_m148653941 ACVR2A 582.07/653.55 470.64/1227.3 617.81 848.99 0.45795 1.4442e+05 57998 0.95993 0.83058 0.16942 0.33884 0.62453 True -ABT1_p26597240 ABT1 119.06/378.28 185.48/128.74 248.67 157.11 -0.65907 17603 9449.8 0.95907 0.16876 0.83124 0.33752 0.62326 False -ADRBK2_p25961098 ADRBK2 2034.3/822.26 1083.2/1041.9 1428.3 1062.6 -0.42635 3.6769e+05 1.4761e+05 0.95215 0.17051 0.82949 0.34102 0.62738 False -ACAT2_m160183956 ACAT2 796.67/1024.7 1444.1/813.64 910.7 1128.9 0.30954 1.1237e+05 52638 0.95096 0.82918 0.17082 0.34164 0.62738 True -AATK_p79102311 AATK 107.3/326.77 531.84/160.78 217.04 346.31 0.67165 46463 18567 0.94873 0.81853 0.18147 0.36294 0.6361 True -ABCB8_m150725669 ABCB8 1206.8/754.78 952.64/616.24 980.77 784.44 -0.32188 79365 42941 0.94744 0.17171 0.82829 0.34341 0.62949 False -ABCC1_m16101788 ABCC1 665.85/525.68 475.69/490.36 595.77 483.03 -0.30208 4965.8 14200 0.94608 0.17205 0.82795 0.34411 0.6296 False -ADAMTS5_p28338524 ADAMTS5 902.5/1040.7 1009.4/594.5 971.6 801.96 -0.27652 47816 32253 0.94461 0.17243 0.82757 0.34486 0.6296 False -AGPAT5_p6566182 AGPAT5 483.59/413.8 498.4/591.06 448.69 544.73 0.27926 3364.2 10359 0.94362 0.82732 0.17268 0.34537 0.6296 True -ABLIM2_m8108294 ABLIM2 1042.1/2282.1 1859.2/2234.4 1662.1 2046.8 0.30019 4.1955e+05 1.6951e+05 0.93436 0.82494 0.17506 0.35013 0.631 True -ADAM12_m128018982 ADAM12 643.8/353.41 417.02/350.17 498.61 383.6 -0.37745 22199 15166 0.93401 0.17515 0.82485 0.3503 0.631 False -AGAP3_p150783866 AGAP3 352.77/234.43 310.4/429.14 293.6 369.77 0.33177 7026 6650.8 0.934 0.82482 0.17518 0.35036 0.631 True -ACTN1_m69445694 ACTN1 311.61/630.46 514.81/110.43 471.04 312.62 -0.58989 66296 29388 0.93365 0.17524 0.82476 0.35049 0.631 False -ADK_p76074475 ADK 198.43/28.415 249.83/114.44 113.42 182.13 0.67851 11810 5433.1 0.93218 0.81278 0.18722 0.37443 0.64631 True -ADAR_p154574126 ADAR 342.48/403.14 234.69/339.88 372.81 287.28 -0.37482 3685.9 8429.9 0.93159 0.17577 0.82423 0.35155 0.631 False -ADCK4_m41220243 ADCK4 830.48/864.89 1349.5/740.97 847.68 1045.2 0.3019 92864 44971 0.93153 0.8242 0.1758 0.35159 0.631 True -AGBL5_p27275820 AGBL5 423.32/412.02 224.6/426.85 417.67 325.72 -0.35775 10258 9796.5 0.92903 0.17644 0.82356 0.35287 0.631 False -AGAP2_m58128438 AGAP2 817.25/896.85 1170.9/832.52 857.05 1001.7 0.22479 30214 24260 0.92887 0.82352 0.17648 0.35296 0.631 True -AATF_m35307525 AATF 405.68/259.29 324.28/166.5 332.49 245.39 -0.43668 11581 8808.4 0.92863 0.17654 0.82346 0.35308 0.631 False -ACTL6A_p179287976 ACTL6A 317.49/985.65 1232.8/599.07 651.57 915.92 0.49065 2.12e+05 81125 0.9281 0.82134 0.17866 0.35732 0.63178 True -ACVRL1_p52306883 ACVRL1 1552.2/1129.5 1162.1/1157 1340.8 1159.5 -0.20944 44671 38239 0.92724 0.1769 0.8231 0.3538 0.63116 False -AFAP1L2_p116164194 AFAP1L2 590.89/365.85 606.29/552.15 478.37 579.22 0.27547 13394 11881 0.92528 0.82259 0.17741 0.35482 0.63146 True -ABTB1_p127395252 ABTB1 526.21/399.59 411.34/771.3 462.9 591.32 0.35256 36401 19284 0.92477 0.82238 0.17762 0.35524 0.63146 True -ACHE_m100491729 ACHE 486.53/831.14 860.53/730.1 658.84 795.32 0.27124 33943 21903 0.92221 0.82179 0.17821 0.35642 0.63178 True -AGBL5_m27275950 AGBL5 354.24/280.6 204.41/275.79 317.42 240.1 -0.4013 2629.6 7049 0.92117 0.17848 0.82152 0.35696 0.63178 False -ACTN4_m39138399 ACTN4 793.73/710.38 763.38/989.87 752.05 876.63 0.22085 14562 18402 0.91829 0.82077 0.17923 0.35847 0.6327 True -ACVR1C_m158401016 ACVR1C 448.31/458.19 456.13/637.41 453.25 546.77 0.27008 8239.7 10476 0.91368 0.81956 0.18044 0.36089 0.63576 True -ABL1_m133729456 ABL1 1145/985.65 1194.3/478.92 1065.3 836.6 -0.34834 1.3428e+05 62837 0.91256 0.18074 0.81926 0.36147 0.63576 False -ADRA1A_p26722419 ADRA1A 567.37/507.92 500.3/370.2 537.65 435.25 -0.30419 5114.7 12668 0.90978 0.18147 0.81853 0.36294 0.6361 False -AHNAK2_p105423957 AHNAK2 676.14/552.32 313.55/654 614.23 483.78 -0.3438 32810 20731 0.90607 0.18245 0.81755 0.3649 0.63841 False -ACVR1_p158655958 ACVR1 410.09/360.52 675.05/325.57 385.31 500.31 0.37596 31149 16213 0.90322 0.81657 0.18343 0.36687 0.64073 True -ABLIM2_m8108279 ABLIM2 673.2/600.27 476.32/574.47 636.74 525.4 -0.27681 3738 15291 0.90039 0.18396 0.81604 0.36791 0.64111 False -ADRM1_p60878704 ADRM1 273.4/671.31 363.39/323.28 472.35 343.34 -0.45909 39986 20641 0.89954 0.18418 0.81582 0.36836 0.64111 False -ACBD6_p180471263 ACBD6 355.71/285.93 278.22/212.28 320.82 245.25 -0.38612 2304.5 7133 0.89497 0.1854 0.8146 0.37081 0.64423 False -AFF3_m100623914 AFF3 242.53/216.67 112.3/222.01 229.6 167.15 -0.45561 3176.2 4916.9 0.89214 0.18616 0.81384 0.37232 0.64573 False -ACBD6_p180471286 ACBD6 723.18/1007 934.98/428.56 865.07 681.77 -0.34308 84248 42419 0.89002 0.18673 0.81327 0.37346 0.64631 False -ADRBK1_p67034172 ADRBK1 1321.4/1795.5 1478.8/1237.1 1558.4 1357.9 -0.19856 70796 51201 0.88616 0.18777 0.81223 0.37553 0.64631 False -ADCK1_p78285311 ADCK1 868.69/701.5 720.48/603.65 785.1 662.06 -0.24556 10401 19304 0.88551 0.18794 0.81206 0.37588 0.64631 False -ACP1_m264983 ACP1 2104.9/1738.7 1569.7/1866.5 1921.8 1718.1 -0.16156 55549 53370 0.88174 0.18896 0.81104 0.37792 0.64866 False -ACBD6_p180471251 ACBD6 488/204.23 565.28/348.46 346.12 456.87 0.39952 31883 15802 0.88105 0.81029 0.18971 0.37941 0.64903 True -ACD_p67694333 ACD 307.2/243.3 157.09/256.91 275.25 207 -0.40941 3511.6 6015.7 0.88058 0.18927 0.81073 0.37855 0.64866 False -ACP1_p272042 ACP1 862.81/1184.6 890.82/876.01 1023.7 883.41 -0.21239 25934 25936 0.87101 0.19187 0.80813 0.38375 0.65417 False -ACAT2_p160183133 ACAT2 520.33/671.31 659.91/739.26 595.82 699.58 0.23126 7272.3 14202 0.8707 0.80804 0.19196 0.38392 0.65417 True -ADNP2_p77891013 ADNP2 742.28/607.37 525.53/601.93 674.83 563.73 -0.25909 6009.6 16312 0.86985 0.19219 0.80781 0.38438 0.65417 False -ACTN4_p39138465 ACTN4 1705.1/1083.3 1088.9/1277.7 1394.2 1183.3 -0.23643 1.0554e+05 59565 0.86409 0.19377 0.80623 0.38754 0.65842 False -ACTN4_p39138381 ACTN4 657.03/635.79 678.21/829.66 646.41 753.93 0.22167 5847.6 15550 0.86227 0.80573 0.19427 0.38854 0.659 True -ADCK2_p140373137 ADCK2 194.02/53.278 121.76/25.176 123.65 73.469 -0.74318 7284.5 4075.4 0.86081 0.19467 0.80533 0.38934 0.65924 False -A1CF_p52603829 A1CF 111.71/252.18 209.46/269.5 181.95 239.48 0.39447 5834.5 4475.7 0.85992 0.80444 0.19556 0.39111 0.661 True -ADRB2_p148206474 ADRB2 148.46/442.21 418.91/358.19 295.33 388.55 0.39458 22495 11835 0.85682 0.80358 0.19642 0.39285 0.66181 True -ADD3_m111860450 ADD3 1597.7/1502.5 1651/1794.9 1550.1 1723 0.15246 7446.8 41157 0.85219 0.80294 0.19706 0.39411 0.66282 True -ABCF1_m30539291 ABCF1 216.07/289.48 192.42/498.37 252.78 345.4 0.44887 24748 11897 0.84914 0.80005 0.19995 0.3999 0.66917 True -ABCF1_p30539272 ABCF1 1111.2/1038.9 948.23/922.36 1075.1 935.29 -0.20075 1473.9 27389 0.84464 0.19916 0.80084 0.39831 0.66838 False -AATK_m79102320 AATK 1474.3/1163.2 1218.9/1844.1 1318.8 1531.5 0.21562 1.2192e+05 63562 0.84385 0.80062 0.19938 0.39875 0.66838 True -ADD1_m2877771 ADD1 530.62/809.83 815.74/747.27 670.23 781.5 0.2213 20661 17680 0.83689 0.79867 0.20133 0.40265 0.67266 True -AGL_m100327112 AGL 1142.1/957.24 1162.1/1210.2 1049.7 1186.1 0.17618 9120.3 26669 0.83566 0.79833 0.20167 0.40335 0.67269 True -ACAD11_p132378489 ACAD11 749.63/1323.1 848.55/870.86 1036.4 859.7 -0.26933 82335 44974 0.833 0.20242 0.79758 0.40484 0.67407 False -ADH7_m100350735 ADH7 812.84/664.2 774.73/453.74 738.52 614.24 -0.26545 31282 22450 0.82949 0.20341 0.79659 0.40683 0.67624 False -AAAS_m53715176 AAAS 801.08/158.06 247.31/379.93 479.57 313.62 -0.61114 1.0777e+05 43359 0.82646 0.20427 0.79573 0.40854 0.67796 False -AHNAK_p62303551 AHNAK 51.445/40.847 117.35/31.47 46.146 74.408 0.67757 1871.7 1174.8 0.82454 0.77515 0.22485 0.4497 0.70415 True -ADRBK1_m67034219 ADRBK1 1109.8/1138.4 970.31/999.03 1124.1 984.67 -0.19084 411.13 28781 0.82169 0.20563 0.79437 0.41126 0.68051 False -ADRB2_p148206467 ADRB2 761.39/925.27 649.82/799.34 843.33 724.58 -0.21868 12303 20904 0.82136 0.20572 0.79428 0.41144 0.68051 False -ADNP2_p77890985 ADNP2 776.09/1280.5 1125.5/1259.4 1028.3 1192.4 0.2135 68076 40069 0.82012 0.79393 0.20607 0.41215 0.68056 True -ACAD9_m128598614 ACAD9 676.14/966.12 1724.2/528.12 821.13 1126.2 0.45527 3.7868e+05 1.3976e+05 0.81598 0.78979 0.21021 0.42041 0.68195 True -ADRA1A_m26722434 ADRA1A 1052.4/1303.5 984.82/1087.1 1178 1036 -0.18516 18383 30322 0.8155 0.20739 0.79261 0.41478 0.68056 False -ADNP_p49510949 ADNP 429.2/889.75 567.8/1132.9 659.48 850.36 0.36626 1.3287e+05 54888 0.81476 0.79189 0.20811 0.41623 0.68056 True -ADH7_p100350692 ADH7 585.01/452.87 409.45/913.2 518.94 661.32 0.3492 67807 30721 0.81236 0.79139 0.20861 0.41723 0.68056 True -AFF4_p132272747 AFF4 527.68/113.66 72.552/331.29 320.67 201.92 -0.66466 59590 24616 0.81217 0.20835 0.79165 0.4167 0.68056 False -ADAM12_p128076650 ADAM12 779.03/648.22 424.59/759.86 713.63 592.22 -0.26861 32379 22366 0.81178 0.20846 0.79154 0.41692 0.68056 False -ADH5_p100002584 ADH5 2173.9/1182.8 1374.1/1439 1678.4 1406.6 -0.25472 2.4665e+05 1.1219e+05 0.81146 0.20855 0.79145 0.4171 0.68056 False -ACTR1A_m104248862 ACTR1A 1056.8/2156 1396.2/1228.5 1606.4 1312.3 -0.29153 3.0907e+05 1.3157e+05 0.81082 0.20874 0.79126 0.41747 0.68056 False -ADRB3_p37823958 ADRB3 2221/976.77 1393/1151.8 1598.9 1272.4 -0.32927 4.0156e+05 1.6225e+05 0.81059 0.2088 0.7912 0.4176 0.68056 False -AHNAK_p62303510 AHNAK 473.3/451.09 598.08/493.22 462.19 545.65 0.239 2872.4 10707 0.80657 0.79004 0.20996 0.41992 0.68195 True -ACIN1_p23538730 ACIN1 946.6/470.63 663.07/457.75 708.61 560.41 -0.33799 67176 33874 0.80541 0.21029 0.78971 0.42058 0.68195 False -ADAD1_p123301314 ADAD1 1456.6/1408.3 1311/1241.6 1432.5 1276.3 -0.16642 1786.1 37697 0.80437 0.21059 0.78941 0.42118 0.68195 False -AGBL5_p27275876 AGBL5 440.96/630.46 434.68/457.17 535.71 445.93 -0.2641 9104 12617 0.79931 0.21206 0.78794 0.42411 0.68531 False -ACSS2_m33501222 ACSS2 662.91/777.87 864.95/787.32 720.39 826.14 0.19735 4810.1 17542 0.79841 0.78768 0.21232 0.42463 0.68531 True -AATF_m35306444 AATF 659.97/809.83 880.72/802.2 734.9 841.46 0.19509 7156 17936 0.79565 0.78688 0.21312 0.42623 0.68679 True -ADRBK2_m26000349 ADRBK2 263.11/385.38 578.53/260.34 324.24 419.43 0.37036 29048 14494 0.79066 0.78467 0.21533 0.43066 0.69033 True -ADCK4_m41220249 ADCK4 1481.6/1994.4 2044.1/1811 1738 1927.5 0.14923 79318 57604 0.78959 0.78512 0.21488 0.42977 0.69033 True -AGTPBP1_m88307605 AGTPBP1 727.59/445.76 502.19/471.48 586.67 486.83 -0.26863 20092 16004 0.78923 0.21499 0.78501 0.42998 0.69033 False -ACBD6_m180471277 ACBD6 1175.9/518.58 833.41/473.19 847.24 653.3 -0.37452 1.4046e+05 60826 0.78714 0.2156 0.7844 0.4312 0.69033 False -AATF_m35307516 AATF 518.86/632.24 627.74/308.98 575.55 468.36 -0.29676 28615 18649 0.78499 0.21623 0.78377 0.43246 0.69124 False -AFTPH_m64778646 AFTPH 999.51/939.48 826.47/1464.2 969.49 1145.3 0.24024 1.0258e+05 50469 0.78274 0.78311 0.21689 0.43378 0.69225 True -ABLIM2_m8108265 ABLIM2 1587.5/1193.4 1145.7/1324 1390.4 1234.9 -0.17107 46764 39900 0.77891 0.21802 0.78198 0.43603 0.69473 False -ADRB1_m115803930 ADRB1 440.96/220.22 451.72/361.62 330.59 406.67 0.298 14211 9653.8 0.77431 0.78054 0.21946 0.43892 0.69735 True -AFAP1L2_m116100394 AFAP1L2 665.85/989.2 859.9/518.4 827.53 689.15 -0.26364 55296 32078 0.77262 0.21987 0.78013 0.43975 0.69735 False -AGPAT3_m45379606 AGPAT3 376.29/294.81 332.48/204.84 335.55 268.66 -0.31967 5732.6 7498.1 0.77258 0.21988 0.78012 0.43977 0.69735 False -ACVR1_p158637019 ACVR1 668.79/1074.4 722.37/761.57 871.62 741.97 -0.23205 41524 28298 0.7707 0.22044 0.77956 0.44088 0.69801 False -ADD1_m2877698 ADD1 477.71/403.14 471.27/246.04 440.42 358.66 -0.29555 14073 11456 0.76401 0.22243 0.77757 0.44486 0.70037 False -AAAS_p53714441 AAAS 401.27/580.73 689.56/474.34 491 581.95 0.2447 19632 14178 0.76378 0.7775 0.2225 0.445 0.70037 True -ACTN1_p69445745 ACTN1 868.69/744.12 765.27/632.26 806.41 698.76 -0.20643 8302.4 19888 0.76329 0.22265 0.77735 0.44529 0.70037 False -ADAM10_p59009761 ADAM10 361.59/237.98 184.85/290.67 299.78 237.76 -0.33316 6619.3 6616.3 0.76281 0.22279 0.77721 0.44558 0.70037 False -ADD3_m111860445 ADD3 1267/694.4 1038.4/534.42 980.71 786.43 -0.31815 1.4549e+05 64981 0.7623 0.22294 0.77706 0.44588 0.70037 False -ACVR2B_p38495827 ACVR2B 1159.7/1097.5 1008.2/1576.9 1128.6 1292.5 0.19548 81842 46555 0.75969 0.77628 0.22372 0.44744 0.70171 True -ADRBK2_m25961092 ADRBK2 1675.7/966.12 1316/1752 1320.9 1534 0.21567 1.7338e+05 80755 0.75004 0.77339 0.22661 0.45323 0.70857 True -AFF4_m132272813 AFF4 953.95/749.45 755.81/1234.2 851.7 995 0.22411 67669 36646 0.74858 0.77294 0.22706 0.45411 0.70884 True -AGL_p100327246 AGL 463.01/253.96 201.88/358.19 358.48 280.04 -0.35518 17033 11058 0.74686 0.22757 0.77243 0.45515 0.70935 False -AFF3_p100623867 AFF3 936.31/561.2 664.33/601.36 748.75 632.84 -0.24229 36168 24264 0.74411 0.22841 0.77159 0.45681 0.71083 False -ADD3_m111860456 ADD3 1484.6/879.09 1263/687.19 1181.8 975.12 -0.27712 1.7455e+05 78472 0.73797 0.23027 0.76973 0.46054 0.71551 False -ADCK2_p140373194 ADCK2 1255.3/1015.8 1528/1049.4 1135.6 1288.7 0.18236 71604 43274 0.73618 0.76919 0.23081 0.46162 0.71609 True -ACSL6_m131329874 ACSL6 795.2/1069.1 1364.6/817.07 932.16 1090.8 0.22657 93708 46815 0.7334 0.76834 0.23166 0.46332 0.7176 True -AFAP1L2_p116100376 AFAP1L2 220.48/513.25 374.12/537.85 366.86 455.98 0.31297 28130 14897 0.73016 0.76705 0.23295 0.46591 0.71859 True -ACTR8_m53916091 ACTR8 351.3/781.42 227.75/624.25 566.36 426 -0.41002 85553 37466 0.72943 0.23287 0.76713 0.46574 0.71859 False -ACAT2_m160183168 ACAT2 658.5/261.06 358.35/354.18 459.78 356.26 -0.3671 39494 20261 0.72882 0.23305 0.76695 0.46611 0.71859 False -AEBP1_m44144355 AEBP1 524.74/1969.5 1010.7/842.82 1247.1 926.75 -0.42796 5.2889e+05 1.9784e+05 0.7266 0.23374 0.76626 0.46747 0.71902 False -ADRB1_m115803938 ADRB1 864.28/1175.7 965.26/1358.4 1020 1161.8 0.18766 62873 38178 0.72587 0.76604 0.23396 0.46792 0.71902 True -ACO2_p41903813 ACO2 1045.1/488.39 1190.5/694.06 766.73 942.27 0.29707 1.3909e+05 58898 0.72332 0.76507 0.23493 0.46986 0.71902 True -ACVR1B_m52369187 ACVR1B 1039.2/1157.9 1012.6/942.38 1098.6 977.48 -0.16831 4755.4 28055 0.72287 0.23488 0.76512 0.46976 0.71902 False -ADRB2_p148206462 ADRB2 1352.3/939.48 1104.7/1499.7 1145.9 1302.2 0.18433 81608 46805 0.7225 0.76501 0.23499 0.46999 0.71902 True -AFAP1L2_m116092987 AFAP1L2 878.98/465.3 635.94/975.57 672.14 805.75 0.26122 71621 34700 0.71726 0.76336 0.23664 0.47328 0.72295 True -ACTL7A_m111624669 ACTL7A 554.14/381.83 603.13/481.78 467.99 542.45 0.21262 11105 10939 0.71201 0.76177 0.23823 0.47646 0.72669 True -AFF1_p87967926 AFF1 792.26/1030 1313.5/799.34 911.16 1056.4 0.2132 80230 41932 0.70942 0.76097 0.23903 0.47807 0.72803 True -ADAM10_m59009866 ADAM10 610/626.91 339.42/695.77 618.45 517.6 -0.25638 31819 20475 0.70487 0.24045 0.75955 0.48089 0.73 False -ADARB1_p46595705 ADARB1 1987.3/1898.5 1793.6/2477.5 1942.9 2135.6 0.13637 1.1891e+05 74917 0.70405 0.7593 0.2407 0.4814 0.73 True -ACTN4_p39138403 ACTN4 837.83/1486.5 1100.3/1614.7 1162.1 1357.5 0.22396 1.7134e+05 77026 0.70382 0.75923 0.24077 0.48155 0.73 True -ACO2_p41865164 ACO2 646.74/310.79 666.22/487.5 478.77 576.86 0.26838 36201 19490 0.70263 0.75878 0.24122 0.48243 0.73023 True -AEBP1_p44144293 AEBP1 901.03/1152.6 991.13/1306.9 1026.8 1149 0.16205 40743 30930 0.69475 0.75639 0.24361 0.48721 0.73496 True -ADCK2_m140373177 ADCK2 582.07/804.5 745.08/427.42 693.29 586.25 -0.24156 37597 23739 0.69472 0.24362 0.75638 0.48723 0.73496 False -ACAT2_p160183141 ACAT2 1243.5/843.58 613.86/1148.4 1043.5 881.11 -0.24384 1.1141e+05 54802 0.69387 0.24388 0.75612 0.48776 0.73496 False -ACTR5_m37377155 ACTR5 95.542/369.4 415.76/207.7 232.47 311.73 0.42168 29571 13180 0.69038 0.74965 0.25035 0.50069 0.74006 True -AFMID_p76183465 AFMID 158.75/598.49 317.97/236.88 378.62 277.43 -0.44726 49988 22380 0.69003 0.24509 0.75491 0.49018 0.73555 False -ABI1_m27149743 ABI1 1412.5/703.28 808.8/964.13 1057.9 886.46 -0.25482 1.318e+05 61867 0.68931 0.24531 0.75469 0.49063 0.73555 False -ACTR1A_p104262374 ACTR1A 1337.6/722.81 750.13/991.02 1030.2 870.57 -0.24263 1.0899e+05 53744 0.68856 0.24555 0.75445 0.4911 0.73555 False -ACAD11_m132378578 ACAD11 526.21/593.17 497.77/463.47 559.69 480.62 -0.21931 1414.9 13247 0.687 0.24604 0.75396 0.49208 0.73555 False -ABHD14B_m52004113 ABHD14B 579.13/651.77 926.78/517.82 615.45 722.3 0.23061 43130 24192 0.68696 0.75394 0.24606 0.49212 0.73555 True -ABCB8_p150725675 ABCB8 943.66/756.55 615.12/1467.6 850.11 1041.4 0.29247 1.9045e+05 77545 0.68688 0.75364 0.24636 0.49271 0.73555 True -ADD1_m2877722 ADD1 479.18/362.29 508.5/467.47 420.74 487.98 0.21345 3836.3 9643.7 0.6848 0.75326 0.24674 0.49347 0.73555 True -AGFG1_m228337217 AGFG1 336.6/618.03 361.5/429.14 477.32 395.32 -0.2713 20944 14379 0.68388 0.24702 0.75298 0.49405 0.73555 False -ABL2_p179100556 ABL2 605.59/502.59 842.87/465.18 554.09 654.03 0.23883 38313 21504 0.68149 0.7522 0.2478 0.4956 0.73676 True -ABCF1_p30545605 ABCF1 501.23/387.16 436.58/314.7 444.19 375.64 -0.24125 6966.4 10244 0.67735 0.24909 0.75091 0.49819 0.73951 False -ADCK1_p78285341 ADCK1 1168.5/907.51 879.46/977.86 1038 928.66 -0.16046 19456 26340 0.67388 0.25019 0.74981 0.50039 0.74006 False -ACSS2_m33500940 ACSS2 1655.1/943.03 1069.4/2038.1 1299.1 1553.7 0.2581 3.6137e+05 1.43e+05 0.6735 0.74961 0.25039 0.50078 0.74006 True -ACO2_p41895844 ACO2 264.58/525.68 376.01/576.76 395.13 476.38 0.26918 27119 15035 0.66268 0.74607 0.25393 0.50786 0.74941 True -AFF2_m147924509 AFF2 122/81.694 31.544/115.01 101.85 73.276 -0.46951 2147.7 2043.7 0.65991 0.25466 0.74534 0.50931 0.75045 False -AFAP1L2_m116100466 AFAP1L2 457.13/433.33 488.94/534.42 445.23 511.68 0.20027 658.65 10270 0.65568 0.74398 0.25602 0.51203 0.75335 True -ACP1_p272099 ACP1 352.77/325 345.73/444.01 338.88 394.87 0.21999 2607.8 7581.1 0.64301 0.73988 0.26012 0.52024 0.7643 True -AHCTF1_m247068844 AHCTF1 1106.8/1056.7 1743.1/807.35 1081.8 1275.2 0.23721 2.1956e+05 91572 0.63943 0.73868 0.26132 0.52263 0.76668 True -ADCY1_m45614206 ADCY1 179.32/312.57 409.45/200.84 245.95 305.14 0.31001 15318 8644.5 0.63668 0.73676 0.26324 0.52648 0.7694 True -ACTR1A_p104248833 ACTR1A 804.02/745.9 651.08/1125.5 774.96 888.28 0.19666 57109 31721 0.63626 0.7377 0.2623 0.52461 0.76845 True -A1CF_m52603842 A1CF 402.74/431.56 198.1/489.22 417.15 343.66 -0.27886 21395 13500 0.6329 0.2634 0.7366 0.5268 0.7694 False -ACVR1C_m158401097 ACVR1C 961.3/788.52 982.3/952.68 874.91 967.49 0.14496 7682 21777 0.62737 0.73479 0.26521 0.53042 0.77356 True -ACVR1C_p158443795 ACVR1C 1481.6/1044.3 1328.7/1444.2 1262.9 1386.4 0.13447 51161 38897 0.62607 0.73437 0.26563 0.53127 0.77367 True -ACTL6A_p179287920 ACTL6A 1202.4/1166.8 1205/946.39 1184.6 1075.7 -0.13898 17036 30511 0.62335 0.26653 0.73347 0.53305 0.77462 False -AFF4_m132272866 AFF4 1199.4/1466.9 1408.1/1490.5 1333.2 1449.3 0.12044 19588 34799 0.62272 0.73326 0.26674 0.53347 0.77462 True -ABI1_p27149733 ABI1 123.47/470.63 249.2/199.12 297.05 224.16 -0.40459 30757 14617 0.61835 0.26817 0.73183 0.53634 0.77589 False -AGPAT3_p45379613 AGPAT3 598.24/305.46 466.86/275.22 451.85 371.04 -0.28358 30611 17164 0.61746 0.26847 0.73153 0.53693 0.77589 False -ADAD1_m123301308 ADAD1 780.5/754.78 553.92/1272.5 767.64 913.23 0.25024 1.2927e+05 55640 0.6172 0.7313 0.2687 0.53741 0.77589 True -ACSS2_p33470688 ACSS2 880.45/621.58 671.9/1040.8 751.02 856.35 0.18911 50775 29175 0.61667 0.73127 0.26873 0.53746 0.77589 True -ADAMTS5_m28338537 ADAMTS5 981.87/1282.2 680.73/1288.6 1132.1 984.64 -0.20108 1.1492e+05 57645 0.61399 0.26961 0.73039 0.53923 0.77732 False -ABL2_p179095768 ABL2 535.03/577.18 807.54/470.33 556.11 638.94 0.19997 28871 18392 0.61074 0.72931 0.27069 0.54138 0.77843 True -ADAM12_p128076618 ADAM12 120.53/232.65 192.42/234.59 176.59 213.51 0.27249 3587.3 3672.2 0.60923 0.72833 0.27167 0.54334 0.77843 True -AES_p3061186 AES 238.12/266.39 169.71/461.18 252.26 315.44 0.32135 21438 10786 0.60843 0.72648 0.27352 0.54704 0.77843 True -AFTPH_p64778754 AFTPH 849.59/861.33 762.11/771.87 855.46 766.99 -0.15729 58.314 21239 0.60703 0.27191 0.72809 0.54383 0.77843 False -ABL1_p133729469 ABL1 930.43/1090.4 1011.3/1203.3 1010.4 1107.3 0.13196 15614 25562 0.60593 0.72772 0.27228 0.54456 0.77843 True -ABI1_p27112180 ABI1 455.66/380.05 335.63/381.64 417.86 358.64 -0.21991 1958.4 9570.3 0.60534 0.27248 0.72752 0.54495 0.77843 False -ACTR3_p114684946 ACTR3 770.21/548.77 752.65/367.91 659.49 560.28 -0.23481 49265 27022 0.60358 0.27306 0.72694 0.54612 0.77843 False -ADD1_p2877743 ADD1 573.25/1397.7 728.68/910.34 985.46 819.51 -0.26574 1.7817e+05 75963 0.6025 0.27342 0.72658 0.54684 0.77843 False -ACRC_p70814187 ACRC 961.3/1097.5 959.58/905.19 1029.4 932.39 -0.14268 5380 26097 0.60062 0.27405 0.72595 0.54809 0.77843 False -ACIN1_p23538719 ACIN1 640.86/559.42 711.64/632.26 600.14 671.95 0.16279 3233.6 14317 0.60014 0.72579 0.27421 0.54841 0.77843 True -AFF2_m147967470 AFF2 892.21/1278.7 893.97/1599.8 1085.4 1246.9 0.19988 1.619e+05 72420 0.59993 0.72572 0.27428 0.54857 0.77843 True -AGTPBP1_p88307705 AGTPBP1 655.56/449.31 451.72/516.68 552.44 484.2 -0.18985 11690 13056 0.59722 0.27518 0.72482 0.55036 0.7796 False -AGFG1_p228337142 AGFG1 0/42.623 0/78.389 21.311 39.194 0.84922 1990.4 897.03 0.59709 0.63864 0.36136 0.72272 0.86416 True -AFTPH_p64778649 AFTPH 1724.2/1500.7 1326.8/1650.7 1612.4 1488.8 -0.11505 38727 43003 0.59634 0.27547 0.72453 0.55095 0.7796 False -AFF1_m87967374 AFF1 257.23/191.8 170.34/379.93 224.51 275.13 0.29215 12052 7214.6 0.59595 0.72326 0.27674 0.55348 0.78097 True -AFF2_m147891407 AFF2 355.71/374.72 271.91/602.51 365.22 437.21 0.25892 27413 14631 0.5952 0.72379 0.27621 0.55241 0.78056 True -ADRB3_p37823913 ADRB3 454.19/381.83 525.53/425.7 418.01 475.62 0.18585 3800.5 9574.2 0.58875 0.72198 0.27802 0.55604 0.78336 True -AFMID_p76183445 AFMID 911.32/856.01 938.13/1003.6 883.66 970.87 0.13563 1836.6 22020 0.58768 0.72163 0.27837 0.55674 0.78336 True -ABHD14B_p52004019 ABHD14B 41.156/284.15 169.08/259.77 162.65 214.42 0.39653 16818 7840.3 0.58468 0.71105 0.28895 0.5779 0.79519 True -AGPAT5_m6566212 AGPAT5 2451.7/2452.6 2151.3/2447.8 2452.2 2299.6 -0.092657 21972 68578 0.58273 0.28004 0.71996 0.56008 0.78694 False -ACLY_p40070039 ACLY 314.55/252.18 88.955/359.9 283.37 224.43 -0.33509 19325 10584 0.57916 0.28124 0.71876 0.56248 0.78852 False -ABHD14B_m52004073 ABHD14B 473.3/339.21 388.63/312.41 406.25 350.52 -0.21232 5947.5 9275.2 0.57872 0.28139 0.71861 0.56278 0.78852 False -AFF1_m87967978 AFF1 549.73/365.85 375.38/697.49 457.79 536.43 0.22826 34392 18526 0.57781 0.71819 0.28181 0.56361 0.78858 True -ACSS2_m33470722 ACSS2 734.94/358.74 541.93/378.78 546.84 460.36 -0.24786 42035 22618 0.57532 0.28254 0.71746 0.56508 0.78884 False -ADAR_p154574157 ADAR 1030.4/522.13 676.31/663.73 776.25 670.02 -0.21203 64619 34248 0.57406 0.28296 0.71704 0.56593 0.78884 False -ABL2_p179100578 ABL2 623.23/570.08 549.5/506.95 596.65 528.23 -0.17541 1158.8 14224 0.57371 0.28308 0.71692 0.56616 0.78884 False -ABHD14B_p52004076 ABHD14B 2079.9/1886.1 2073.1/1622.7 1983 1847.9 -0.10171 60105 56127 0.57007 0.28431 0.71569 0.56863 0.79112 False -ADAMTS5_m28338702 ADAMTS5 602.65/497.27 627.1/602.51 549.96 614.81 0.16054 2927.6 12991 0.56896 0.71531 0.28469 0.56938 0.79112 True -AGAP2_p58128423 AGAP2 421.85/511.47 400.62/414.83 466.66 407.72 -0.19434 2058.5 10822 0.56659 0.2855 0.7145 0.571 0.7914 False -ADI1_m3523239 ADI1 579.13/316.12 406.29/636.84 447.62 521.57 0.22011 30581 17082 0.56575 0.71413 0.28587 0.57174 0.7914 True -ACTR5_m37377161 ACTR5 342.48/548.77 531.21/203.12 445.62 367.17 -0.2787 37549 19370 0.56516 0.28598 0.71402 0.57196 0.7914 False -ADRBK2_m26000370 ADRBK2 711.42/431.56 571.59/423.41 571.49 497.5 -0.19965 25069 17395 0.56098 0.28741 0.71259 0.57481 0.79333 False -ADAM10_p58974481 ADAM10 1339.1/1152.6 1468.1/1225 1245.8 1346.6 0.1121 23459 32271 0.56078 0.71253 0.28747 0.57495 0.79333 True -ACTL6B_p100253094 ACTL6B 945.13/1408.3 1386.1/645.99 1176.7 1016 -0.21164 1.9057e+05 83712 0.55546 0.28929 0.71071 0.57858 0.79519 False -AHCY_m32883267 AHCY 789.32/671.31 723.63/588.77 730.31 656.2 -0.15415 8028.3 17811 0.55531 0.28934 0.71066 0.57868 0.79519 False -AGTPBP1_p88307675 AGTPBP1 1175.9/1147.3 909.11/1223.3 1161.6 1066.2 -0.12347 24887 29852 0.55193 0.2905 0.7095 0.58099 0.79727 False -ADCK3_p227149079 ADCK3 2312.1/1088.7 1301.5/1652.5 1700.4 1477 -0.20307 4.05e+05 1.6541e+05 0.54929 0.2914 0.7086 0.58281 0.79866 False -AEBP1_m44144289 AEBP1 1960.8/2106.3 1629/2164 2033.5 1896.5 -0.10062 76855 62737 0.54723 0.29211 0.70789 0.58422 0.79877 False -ADRBK2_p26040592 ADRBK2 426.26/607.37 258.66/615.09 516.82 436.88 -0.24191 39961 21402 0.54684 0.29224 0.70776 0.58449 0.79877 False -ACTN4_m39138527 ACTN4 214.6/159.84 251.09/38.336 187.22 144.72 -0.36926 12066 6634.6 0.54379 0.29329 0.70671 0.58658 0.80054 False -AHRR_m353878 AHRR 1.4699/83.47 0/62.94 42.47 31.47 -0.4209 2671.3 1392.8 0.54162 0.29404 0.70596 0.58808 0.80117 False -ABCC1_m16101777 ABCC1 699.66/728.14 938.13/639.13 713.9 788.63 0.14344 22554 19096 0.5408 0.70568 0.29432 0.58865 0.80117 True -AHNAK_p62303477 AHNAK 655.56/500.82 554.55/475.48 578.19 515.02 -0.16662 7549.5 13735 0.53903 0.29493 0.70507 0.58987 0.80117 False -A1CF_p52601638 A1CF 1093.6/815.16 777.89/1377.2 954.37 1077.6 0.17498 1.0919e+05 52388 0.53823 0.70479 0.29521 0.59043 0.80117 True -ACTRT3_p169487280 ACTRT3 1469.9/1001.6 1264.9/975 1235.8 1120 -0.14182 75827 46596 0.53639 0.29584 0.70416 0.59169 0.80117 False -ACVR2A_p148653997 ACVR2A 1603.6/1811.5 1557.7/1627.9 1707.5 1592.8 -0.10034 12031 45836 0.53615 0.29593 0.70407 0.59186 0.80117 False -AFF3_p100623873 AFF3 318.96/438.66 435.31/210.56 378.81 322.94 -0.22956 16210 11124 0.53008 0.29803 0.70197 0.59606 0.80226 False -ACVRL1_p52306287 ACVRL1 1687.4/1525.5 1391.1/2109.1 1606.5 1750.1 0.12345 1.3541e+05 73689 0.52904 0.70161 0.29839 0.59678 0.80226 True -ADIRF_p88728292 ADIRF 1074.5/950.13 1100.9/743.84 1012.3 922.37 -0.13409 35740 28990 0.52821 0.29868 0.70132 0.59736 0.80226 False -ADD3_m111860540 ADD3 1167.1/1316 1367.1/901.19 1241.5 1134.2 -0.13038 59821 41372 0.52785 0.2988 0.7012 0.5976 0.80226 False -ADRA1A_p26722329 ADRA1A 1571.3/1456.3 1434/1382.4 1513.8 1408.2 -0.10423 3973.3 40085 0.52735 0.29897 0.70103 0.59795 0.80226 False -AAAS_p53714405 AAAS 1644.8/1520.2 1395.5/1553.5 1582.5 1474.5 -0.10191 10116 42116 0.52625 0.29936 0.70064 0.59871 0.80226 False -ACRC_p70800697 ACRC 232.24/681.96 275.7/467.47 457.1 371.59 -0.2981 59757 26969 0.52615 0.29939 0.70061 0.59879 0.80226 False -AGFG1_p228337168 AGFG1 667.32/667.76 492.09/709.5 667.54 600.8 -0.15173 11817 16116 0.52571 0.29954 0.70046 0.59909 0.80226 False -ADRBK2_p25960997 ADRBK2 1311.1/429.78 1237.2/823.37 870.45 1030.3 0.24293 2.37e+05 93436 0.52285 0.6988 0.3012 0.60241 0.80403 True -AEN_m89169507 AEN 567.37/737.02 333.11/790.18 652.19 561.65 -0.21528 59424 30278 0.52055 0.30134 0.69866 0.60268 0.80403 False -ADARB2_m1779246 ADARB2 1863.8/1585.9 1761.4/1464.2 1724.9 1612.8 -0.096827 41390 46354 0.52035 0.30141 0.69859 0.60282 0.80403 False -AGAP2_p58129143 AGAP2 756.98/500.82 792.4/260.91 628.9 526.66 -0.25553 87024 39063 0.51878 0.30196 0.69804 0.60391 0.80441 False -ABT1_m26597238 ABT1 488/367.62 312.29/441.15 427.81 376.72 -0.18301 7773.9 9824.3 0.51545 0.30312 0.69688 0.60624 0.80644 False -ADH7_p100349706 ADH7 464.48/394.26 171.6/547.01 429.37 359.3 -0.25636 36465 18731 0.51365 0.30375 0.69625 0.6075 0.80704 False -ADRA1A_p26722412 ADRA1A 1012.7/983.87 1008.8/825.66 998.31 917.23 -0.12208 8592.9 25221 0.51056 0.30483 0.69517 0.60966 0.80883 False -AGAP3_m150784003 AGAP3 1387.6/1120.6 1214.5/1111.2 1254.1 1162.8 -0.10892 20481 32510 0.50621 0.30636 0.69364 0.61271 0.81147 False -ADCK5_p145597774 ADCK5 576.19/541.66 670.64/303.83 558.93 487.23 -0.19767 33935 20130 0.5054 0.30664 0.69336 0.61328 0.81147 False -ACAD11_p132378547 ACAD11 51.445/465.3 251.09/143.05 258.37 197.07 -0.38901 45737 18984 0.50398 0.30714 0.69286 0.61428 0.81172 False -AGL_p100327114 AGL 47.036/147.4 239.74/30.898 97.22 135.32 0.47289 13422 5734.9 0.50309 0.65854 0.34146 0.68293 0.84021 True -ACVR1_m158637054 ACVR1 291.03/866.66 570.32/389.08 578.85 479.7 -0.27053 91049 39518 0.50227 0.30774 0.69226 0.61548 0.81224 False -AFF4_m132272841 AFF4 241.06/637.57 282.64/802.2 439.31 542.42 0.30354 1.0679e+05 42342 0.50107 0.68671 0.31329 0.62659 0.81924 True -ACRC_p70800656 ACRC 258.7/685.52 533.73/560.74 472.11 547.24 0.21263 45726 22550 0.5003 0.69131 0.30869 0.61738 0.8126 True -ADCY1_p45614284 ADCY1 435.08/504.37 371.59/463.47 469.73 417.53 -0.16955 3310.3 10901 0.49993 0.30856 0.69144 0.61713 0.8126 False -ADD3_p111860545 ADD3 1381.7/1209.4 1247.3/1161 1295.5 1204.1 -0.10551 9280.7 33708 0.49803 0.30923 0.69077 0.61846 0.81295 False -AGFG1_m228337208 AGFG1 671.73/781.42 554.55/766.72 726.57 660.64 -0.13705 14262 17710 0.49547 0.31013 0.68987 0.62027 0.81426 False -ADNP_m49520451 ADNP 1649.2/783.19 773.47/1332 1216.2 1052.8 -0.20802 2.6549e+05 1.0944e+05 0.49427 0.31056 0.68944 0.62111 0.81429 False -ADRM1_p60878682 ADRM1 1011.3/713.93 792.4/1100.3 862.6 946.35 0.13353 45805 29559 0.48712 0.68691 0.31309 0.62617 0.81924 True -ADAP1_m975116 ADAP1 586.48/335.65 379.8/433.71 461.07 406.75 -0.1804 16455 12603 0.48382 0.31426 0.68574 0.62851 0.81924 False -AFTPH_p64778890 AFTPH 620.29/529.23 366.55/657.44 574.76 511.99 -0.16653 23227 16839 0.48371 0.31429 0.68571 0.62859 0.81924 False -AES_m3057717 AES 999.51/1026.5 938.13/1242.2 1013 1090.2 0.10581 23297 25635 0.48195 0.68508 0.31492 0.62984 0.81924 True -ADRB3_p37823900 ADRB3 180.79/239.75 279.48/205.41 210.27 242.45 0.20451 2240.7 4458.9 0.48184 0.68478 0.31522 0.63043 0.81924 True -ADCY1_m45614270 ADCY1 667.32/614.48 543.2/873.15 640.9 708.17 0.14379 27915 19573 0.48085 0.68469 0.31531 0.63063 0.81924 True -ACTRT3_m169487252 ACTRT3 283.69/854.23 348.88/597.93 568.96 473.41 -0.26473 96887 41290 0.47513 0.31735 0.68265 0.6347 0.82346 False -ABCC1_p16101667 ABCC1 320.43/248.63 247.31/397.67 284.53 322.49 0.18006 6940.6 6474.6 0.4717 0.68136 0.31864 0.63727 0.82573 True -ABCC1_p16101721 ABCC1 1480.2/1214.7 882.61/1568.3 1347.5 1225.5 -0.13678 1.3517e+05 68533 0.46592 0.32064 0.67936 0.64127 0.82911 False -AFMID_m76183481 AFMID 701.13/978.55 617.01/908.62 839.84 762.82 -0.1386 40500 27372 0.46554 0.32077 0.67923 0.64155 0.82911 False -ABL2_m179100554 ABL2 1055.4/722.81 915.42/1007.6 889.09 961.52 0.11286 29773 24704 0.4608 0.67753 0.32247 0.64494 0.8294 True -AAK1_m69870049 AAK1 535.03/825.82 437.21/1147.8 680.42 792.5 0.21968 1.4737e+05 60100 0.45717 0.67533 0.32467 0.64934 0.8294 True -ADK_p76074446 ADK 1505.1/451.09 653.6/991.02 978.12 822.31 -0.25005 3.0622e+05 1.1851e+05 0.45682 0.3239 0.6761 0.6478 0.8294 False -ACP1_m264989 ACP1 1456.6/1333.7 1753.9/1236.5 1395.2 1495.2 0.099788 70699 47970 0.45653 0.67599 0.32401 0.64801 0.8294 True -ADNP2_p77875511 ADNP2 1293.5/1303.5 1006.9/1416.1 1298.5 1211.5 -0.099961 41896 36495 0.45537 0.32442 0.67558 0.64885 0.8294 False -ADAD1_m123301345 ADAD1 514.45/468.85 733.09/374.21 491.65 553.65 0.17101 32720 18552 0.45517 0.67546 0.32454 0.64908 0.8294 True -ACTR5_p37377204 ACTR5 291.03/305.46 359.61/310.69 298.25 335.15 0.16776 650.16 6577.1 0.45502 0.67542 0.32458 0.64917 0.8294 True -A1CF_m52595977 A1CF 313.08/486.61 557.08/100.13 399.85 328.6 -0.28232 59727 25984 0.45424 0.32483 0.67517 0.64966 0.8294 False -ACSL6_p131329878 ACSL6 908.38/1435 998.7/1151.8 1171.7 1075.2 -0.12379 75183 45155 0.45376 0.325 0.675 0.65 0.8294 False -ACD_p67694194 ACD 1439/1046 1161.5/1155.8 1242.5 1158.6 -0.10075 38614 34322 0.45277 0.32536 0.67464 0.65071 0.8294 False -ABCC1_p16043597 ABCC1 307.2/412.02 305.35/332.44 359.61 318.89 -0.17285 2930.1 8098.6 0.45251 0.32545 0.67455 0.6509 0.8294 False -ADPRHL2_m36554563 ADPRHL2 1209.7/420.9 786.72/609.95 815.3 698.33 -0.22312 1.6337e+05 67877 0.4506 0.32614 0.67386 0.65228 0.8301 False -ABT1_m26597359 ABT1 358.65/394.26 593.04/268.35 376.45 430.69 0.19371 26672 14572 0.44933 0.67311 0.32689 0.65379 0.83096 True -AEBP2_m19615553 AEBP2 1371.4/534.56 865.58/796.48 952.97 831.03 -0.19732 1.7626e+05 74721 0.44657 0.32759 0.67241 0.65519 0.83141 False -ADRA1B_p159343940 ADRA1B 1074.5/1252 1269.4/1211.3 1163.3 1240.3 0.092473 8724.8 29900 0.4457 0.67209 0.32791 0.65581 0.83141 True -ACVR2B_p38518854 ACVR2B 2146/1630.3 1712.9/1851 1888.2 1781.9 -0.083494 71255 57928 0.44137 0.32947 0.67053 0.65895 0.83325 False -ACD_p67694350 ACD 543.85/245.08 295.89/388.51 394.47 342.2 -0.20451 24461 14138 0.44042 0.32982 0.67018 0.65963 0.83325 False -AGAP3_p150783915 AGAP3 837.83/719.26 374.75/993.88 778.54 684.31 -0.18586 99345 45865 0.44025 0.32988 0.67012 0.65976 0.83325 False -ADAM12_m128019025 ADAM12 560.02/861.33 956.43/613.95 710.68 785.19 0.14365 52020 28860 0.43861 0.66952 0.33048 0.66095 0.8337 True -ACTN1_p69392370 ACTN1 2125.4/1632.1 1962.7/1996.9 1878.8 1979.8 0.075535 61138 54367 0.43334 0.66762 0.33238 0.66477 0.83695 True -ACVR1B_m52369211 ACVR1B 1512.5/1339.1 1062.4/1594.1 1425.8 1328.3 -0.10214 78190 51064 0.43157 0.33303 0.66697 0.66605 0.83695 False -ADCY1_p45614294 ADCY1 149.93/312.57 287.69/239.74 231.25 263.71 0.18878 7187.5 5700 0.43005 0.66606 0.33394 0.66789 0.83695 True -ADRB3_p37823952 ADRB3 1233.2/1458.1 1519.8/1332.6 1345.6 1426.2 0.083838 21399 35162 0.4297 0.66629 0.33371 0.66742 0.83695 True -ACTN1_p69445708 ACTN1 179.32/252.18 249.2/240.32 215.75 244.76 0.18118 1346.9 4588.3 0.42819 0.6655 0.3345 0.669 0.83695 True -ACSL6_p131329824 ACSL6 1027.4/978.55 1338.1/414.83 1003 876.47 -0.19432 2.1371e+05 88139 0.42682 0.33475 0.66525 0.66951 0.83695 False -ACTL6B_p100253037 ACTL6B 1394.9/816.94 642.25/1321.2 1105.9 981.71 -0.17172 1.9875e+05 85092 0.42596 0.33507 0.66493 0.67014 0.83695 False -AAK1_m69870119 AAK1 595.3/488.39 508.5/478.92 541.84 493.71 -0.13396 3076.3 12778 0.42583 0.33512 0.66488 0.67023 0.83695 False -ACBD6_m180471378 ACBD6 592.36/577.18 782.93/494.36 584.77 638.65 0.12695 20876 16231 0.4229 0.66382 0.33618 0.67237 0.83796 True -ABI1_m27149764 ABI1 204.31/332.1 203.15/268.93 268.21 236.04 -0.18361 5164.3 5844.6 0.42122 0.3368 0.6632 0.67359 0.83796 False -ADRA1B_m159343934 ADRA1B 748.16/1200.5 1029.6/751.27 974.35 890.44 -0.12978 70529 39875 0.4202 0.33717 0.66283 0.67434 0.83796 False -AEBP2_p19615440 AEBP2 1030.4/1092.2 878.2/1106.6 1061.3 992.4 -0.096738 13997 26998 0.4193 0.3375 0.6625 0.675 0.83796 False -AHRR_m353898 AHRR 561.49/170.49 199.36/411.97 365.99 305.67 -0.25908 49521 22013 0.41892 0.33764 0.66236 0.67528 0.83796 False -ADPRHL2_p36554568 ADPRHL2 492.41/793.85 555.81/863.99 643.13 709.9 0.14231 46460 25795 0.41577 0.6612 0.3388 0.6776 0.83796 True -AFMID_p76187060 AFMID 454.19/1129.5 697.13/699.21 791.85 698.17 -0.1814 1.1401e+05 50997 0.41523 0.33899 0.66101 0.67797 0.83796 False -ADAP1_m975132 ADAP1 956.89/941.25 1210.7/826.8 949.07 1018.7 0.1021 36901 28194 0.41493 0.6609 0.3391 0.6782 0.83796 True -ADAD1_m123301332 ADAD1 1045.1/1118.8 1071.9/954.4 1082 1013.1 -0.094726 4811 27584 0.41438 0.3393 0.6607 0.67859 0.83796 False -ADNP_p49510837 ADNP 495.35/301.91 336.9/555.02 398.63 445.96 0.16147 21248 13137 0.41291 0.66008 0.33992 0.67985 0.83796 True -ADAM10_p59009767 ADAM10 886.33/751.23 969.05/521.83 818.78 745.44 -0.13521 54564 31673 0.4121 0.34013 0.65987 0.68027 0.83796 False -ACSS2_m33500955 ACSS2 626.17/884.42 517.33/858.27 755.29 687.8 -0.13486 45735 27572 0.40647 0.3422 0.6578 0.6844 0.84098 False -AFAP1L2_p116100433 AFAP1L2 333.66/296.58 346.36/216.28 315.12 281.32 -0.16314 4573.5 6992.3 0.40436 0.34297 0.65703 0.68595 0.84101 False -AHNAK2_m105423801 AHNAK2 1215.6/1655.2 1491.4/1543.7 1435.4 1517.6 0.080286 48996 41520 0.40341 0.65668 0.34332 0.68664 0.84101 True -ACTL6B_m100253076 ACTL6B 924.55/1115.3 1301.5/888.03 1019.9 1094.8 0.10208 51842 34500 0.403 0.65652 0.34348 0.68695 0.84101 True -ADRB2_m148206447 ADRB2 826.07/822.26 880.09/654 824.17 767.05 -0.10349 12783 20376 0.40014 0.34453 0.65547 0.68905 0.84176 False -AGTPBP1_m88307687 AGTPBP1 1988.7/1243.2 1431.5/1579.2 1615.9 1505.4 -0.10221 1.4443e+05 76881 0.39887 0.345 0.655 0.68999 0.84176 False -ADRBK2_m25961108 ADRBK2 1031.8/392.48 904.7/696.34 712.17 800.52 0.1685 1.1305e+05 49230 0.39821 0.65453 0.34547 0.69094 0.84176 True -ADH7_m100350690 ADH7 170.51/715.71 366.55/382.22 443.11 374.38 -0.24254 74373 31601 0.39783 0.34538 0.65462 0.69076 0.84176 False -ADD1_p2877644 ADD1 820.19/1019.4 1227.7/763.29 919.79 995.5 0.114 63843 36630 0.39558 0.65379 0.34621 0.69242 0.84235 True -ACLY_m40070116 ACLY 1009.8/1102.9 1237.2/1004.8 1056.3 1121 0.085596 15670 26858 0.39437 0.65335 0.34665 0.69331 0.84235 True -ADD1_m2877776 ADD1 565.9/438.66 539.41/550.44 502.28 544.92 0.11734 4078 11744 0.39351 0.65303 0.34697 0.69395 0.84235 True -ADAR_m154574108 ADAR 1594.8/1092.2 1489.5/1000.2 1343.5 1244.9 -0.10995 1.2302e+05 64407 0.38874 0.34873 0.65127 0.69747 0.84559 False -ACTR8_m53916103 ACTR8 903.97/1445.6 1482.6/1055.1 1174.8 1268.8 0.11102 1.1903e+05 59831 0.3845 0.6497 0.3503 0.70061 0.84801 True -AATK_m79102325 AATK 930.43/1282.2 1182.3/1161.5 1106.3 1171.9 0.083003 31050 29201 0.38375 0.64942 0.35058 0.70116 0.84801 True -ADPRHL2_p36554580 ADPRHL2 849.59/935.92 934.35/739.83 892.75 837.09 -0.092776 11323 22272 0.373 0.35457 0.64543 0.70915 0.85664 False -ADAM10_m59041689 ADAM10 89.662/99.453 49.84/176.8 94.558 113.32 0.25866 4053.9 2573.9 0.36987 0.63281 0.36719 0.73438 0.86924 True -ACVR1_m158655997 ACVR1 1349.3/2315.8 1698.4/1722.3 1832.6 1710.3 -0.099568 2.3367e+05 1.1095e+05 0.3671 0.35677 0.64323 0.71354 0.8609 False -ADCK3_p227149124 ADCK3 1208.2/1010.5 1032.8/1063.1 1109.4 1047.9 -0.082114 10004 28363 0.36479 0.35764 0.64236 0.71527 0.86195 False -ACP1_m272095 ACP1 511.52/669.53 495.25/783.89 590.52 639.57 0.11492 27071 18398 0.36158 0.64117 0.35883 0.71767 0.8638 True -ADAP1_m975089 ADAP1 471.83/632.24 523.64/662.59 552.03 593.11 0.10337 11259 13046 0.35966 0.64045 0.35955 0.7191 0.86416 True -AEN_p89169516 AEN 1444.9/1069.1 1717.3/994.45 1257 1355.9 0.10914 1.6592e+05 77036 0.3562 0.63915 0.36085 0.72169 0.86416 True -AES_m3057678 AES 868.69/792.07 746.34/1016.2 830.38 881.27 0.085704 19673 20547 0.35499 0.6387 0.3613 0.7226 0.86416 True -ADI1_m3523156 ADI1 768.74/722.81 643.51/948.68 745.78 796.09 0.09407 23809 20091 0.35498 0.6387 0.3613 0.72261 0.86416 True -ACVR1_p158637035 ACVR1 2013.7/1378.1 1688.3/1887.1 1695.9 1787.7 0.075951 1.1087e+05 67284 0.35363 0.63819 0.36181 0.72362 0.86416 True -ACD_m67694316 ACD 279.28/451.09 408.82/385.65 365.18 397.23 0.12105 7514.3 8238.3 0.35311 0.63799 0.36201 0.72402 0.86416 True -ACVR1C_m158401113 ACVR1C 329.25/328.55 306.61/410.25 328.9 358.43 0.12369 2685.5 7333.1 0.34487 0.63488 0.36512 0.73024 0.86924 True -ADCK3_m227149153 ADCK3 1240.6/2438.4 1870/2081.6 1839.5 1975.8 0.10307 3.6988e+05 1.5649e+05 0.34456 0.63479 0.36521 0.73043 0.86924 True -ACSS2_m33501214 ACSS2 680.55/747.67 533.1/1016.2 714.11 774.65 0.11723 59471 31405 0.3416 0.63366 0.36634 0.73268 0.86924 True -ACD_p67694285 ACD 608.53/495.49 551.4/630.54 552.01 590.97 0.098226 4760.4 13045 0.34114 0.6335 0.3665 0.733 0.86924 True -ADI1_p3517660 ADI1 980.4/834.7 943.81/769.01 907.55 856.41 -0.083578 12946 22683 0.33954 0.3671 0.6329 0.7342 0.86924 False -A1CF_p52596023 A1CF 286.62/761.88 493.99/442.87 524.25 468.43 -0.16211 57120 27252 0.33943 0.36714 0.63286 0.73429 0.86924 False -ADARB1_p46595724 ADARB1 343.95/129.64 305.98/227.16 236.8 266.57 0.17018 13035 7737.4 0.33846 0.63118 0.36882 0.73764 0.87104 True -ADRB2_m148206406 ADRB2 829.01/861.33 992.39/795.33 845.17 893.86 0.080714 9969.2 20955 0.33635 0.6317 0.3683 0.7366 0.87085 True -AES_p3061179 AES 268.99/310.79 112.93/397.67 289.89 255.3 -0.18265 20706 11150 0.33263 0.36971 0.63029 0.73942 0.87211 False -ABTB1_p127395834 ABTB1 502.7/861.33 413.86/825.09 682.02 619.47 -0.13855 74431 35814 0.33073 0.37042 0.62958 0.74085 0.87233 False -ADPRHL2_p36554520 ADPRHL2 1102.4/1054.9 1372.8/910.34 1078.7 1141.6 0.081719 54035 36338 0.33007 0.62933 0.37067 0.74135 0.87233 True -ACTN1_p69445679 ACTN1 257.23/131.42 240.37/191.11 194.32 215.74 0.15009 4563.5 4244.2 0.32872 0.62828 0.37172 0.74343 0.87279 True -ADARB1_p46595700 ADARB1 739.35/914.61 992.39/755.85 826.98 874.12 0.079888 21667 20858 0.32641 0.62794 0.37206 0.74411 0.87279 True -ADAM12_m128076635 ADAM12 355.71/708.6 520.48/635.69 532.16 578.09 0.11923 34452 19833 0.32616 0.62782 0.37218 0.74437 0.87279 True -ADCK1_m78285376 ADCK1 1459.6/1179.2 1296.5/1222.2 1319.4 1259.3 -0.067181 21030 34400 0.32391 0.373 0.627 0.746 0.87369 False -AAK1_p69870063 AAK1 485.06/296.58 229.64/651.71 390.82 440.68 0.17281 53417 23728 0.32368 0.62481 0.37519 0.75038 0.87676 True -ACAD9_p128598551 ACAD9 1159.7/1335.5 1120.5/1258.8 1247.6 1189.6 -0.06861 12509 32323 0.32255 0.37352 0.62648 0.74703 0.87387 False -AES_p3061195 AES 539.44/355.19 834.67/193.97 447.32 514.32 0.20095 1.1111e+05 43919 0.31971 0.61916 0.38084 0.76168 0.88069 True -ADCK2_p140373122 ADCK2 579.13/932.37 760.85/653.43 755.75 707.14 -0.095779 34080 23695 0.31578 0.37609 0.62391 0.75217 0.87782 False -AFF1_p87967869 AFF1 718.77/1246.7 1205.6/897.18 982.74 1051.4 0.09734 93468 47678 0.31446 0.62341 0.37659 0.75317 0.87797 True -ACLY_p40070105 ACLY 761.39/475.95 559.6/596.79 618.67 578.19 -0.097466 20715 16778 0.31253 0.37732 0.62268 0.75464 0.8784 False -ADRM1_p60878671 ADRM1 420.38/134.97 307.87/312.41 277.68 310.14 0.15898 20370 10840 0.31182 0.62096 0.37904 0.75808 0.87899 True -ACP1_p272185 ACP1 1625.7/797.4 951.38/1287.4 1211.5 1119.4 -0.11402 1.9974e+05 87436 0.31165 0.37765 0.62235 0.75531 0.8784 False -ABHD14B_p52004124 ABHD14B 858.4/969.67 791.14/1137.5 914.04 964.32 0.077173 33086 26271 0.31021 0.6218 0.3782 0.7564 0.87866 True -ADCK5_m145603095 ADCK5 906.91/1017.6 848.55/1175.3 962.26 1011.9 0.072493 29749 26056 0.30752 0.62078 0.37922 0.75845 0.87899 True -AHNAK2_m105444555 AHNAK2 504.17/277.05 162.14/738.11 390.61 450.13 0.20413 95833 37863 0.30588 0.61147 0.38853 0.77707 0.88952 True -ACTL6A_p179291144 ACTL6A 185.2/449.31 146.37/412.54 317.26 279.45 -0.18244 35151 16414 0.30587 0.37985 0.62015 0.7597 0.87942 False -ACTL7A_p111624686 ACTL7A 341.01/118.99 105.99/296.96 230 201.48 -0.19014 21441 10431 0.29891 0.38251 0.61749 0.76501 0.88352 False -ACVR2A_m148653961 ACVR2A 862.81/431.56 651.08/545.86 647.18 598.47 -0.11272 49264 26802 0.29763 0.38299 0.61701 0.76599 0.88363 False -AEBP2_p19615492 AEBP2 730.53/387.16 403.77/822.22 558.84 613 0.13321 73252 33234 0.29707 0.61638 0.38362 0.76725 0.88386 True -AES_p3056318 AES 1078.9/658.88 797.44/839.39 868.88 818.42 -0.08622 44542 29254 0.29504 0.38398 0.61602 0.76796 0.88386 False -ADAP1_m994106 ADAP1 440.96/660.65 675.05/498.94 550.81 587 0.091646 19820 15282 0.29275 0.61514 0.38486 0.76972 0.88486 True -ADRA1B_m159344006 ADRA1B 501.23/221.99 217.66/434.86 361.61 326.26 -0.14799 31287 15861 0.28397 0.38822 0.61178 0.77644 0.88952 False -AFMID_m76187079 AFMID 395.4/470.63 546.35/251.76 433.01 399.06 -0.11753 23111 14342 0.28378 0.38829 0.61171 0.77658 0.88952 False -ADNP2_p77891053 ADNP2 354.24/321.45 338.16/288.38 337.84 313.27 -0.10862 888.29 7555.2 0.2828 0.38866 0.61134 0.77733 0.88952 False -ADRA1A_p26722465 ADRA1A 555.61/497.27 957.69/225.44 526.44 591.57 0.16797 1.349e+05 53216 0.28232 0.60673 0.39327 0.78654 0.89485 True -ADAR_m154574165 ADAR 673.2/518.58 545.72/579.05 595.89 562.38 -0.083345 6254.9 14204 0.28113 0.3893 0.6107 0.77861 0.88997 False -ACTN1_m69445718 ACTN1 1086.2/632.24 1101.5/731.25 859.24 916.39 0.092804 85806 42831 0.27617 0.60878 0.39122 0.78243 0.8914 True -ACAT2_p160183121 ACAT2 1668.3/1926.9 2248.5/1506.6 1797.6 1877.5 0.062721 1.5434e+05 83802 0.27607 0.60875 0.39125 0.78249 0.8914 True -ACVRL1_m52306307 ACVRL1 818.72/976.77 1041.6/836.53 897.74 939.06 0.064848 16759 22410 0.27601 0.60873 0.39127 0.78254 0.8914 True -AGAP2_m58129192 AGAP2 795.2/726.36 1025.2/378.78 760.78 701.99 -0.11587 1.0565e+05 47642 0.26974 0.39368 0.60632 0.78736 0.89485 False -ACTR3_p114674498 ACTR3 1317/1179.2 1340.6/1059.1 1248.1 1199.9 -0.056823 24561 32337 0.26827 0.39424 0.60576 0.78849 0.89511 False -AHNAK_m62303507 AHNAK 116.12/236.2 292.73/19.454 176.16 156.09 -0.17343 22275 9866.6 0.26114 0.39699 0.60301 0.79398 0.89922 False -ADH7_m100349694 ADH7 811.37/1031.8 1026.5/896.04 921.6 961.25 0.060706 16403 23074 0.26102 0.60296 0.39704 0.79407 0.89922 True -ADAP1_p975103 ADAP1 909.85/710.38 528.05/1215.3 810.11 871.68 0.10555 1.2803e+05 56003 0.26017 0.60251 0.39749 0.79498 0.89922 True -ADK_p75960554 ADK 363.06/825.82 736.88/349.6 594.44 543.24 -0.1297 91032 39787 0.25891 0.39785 0.60215 0.7957 0.89922 False -AHCY_m32883330 AHCY 2009.3/2235.9 2437.1/1938 2122.6 2187.6 0.043454 75125 63974 0.25674 0.60131 0.39869 0.79738 0.9001 True -AGFG1_m228337178 AGFG1 549.73/1021.2 786.72/874.87 785.45 830.79 0.080867 57506 32045 0.25329 0.59998 0.40002 0.80005 0.90139 True -ADAR_p154574043 ADAR 765.8/1046 1073.8/821.08 905.92 947.43 0.064565 35596 26957 0.25282 0.5998 0.4002 0.80041 0.90139 True -ADH5_p100003224 ADH5 555.61/362.29 341.94/643.13 458.95 492.54 0.10167 32022 17756 0.25204 0.59938 0.40062 0.80124 0.90139 True -ACTL6B_m100253971 ACTL6B 483.59/458.19 227.75/640.84 470.89 434.3 -0.11645 42822 21561 0.25025 0.4012 0.5988 0.80239 0.90168 False -ADAMTS5_p28338612 ADAMTS5 114.65/294.81 175.39/274.07 204.73 224.73 0.13386 10549 6401.9 0.24999 0.59658 0.40342 0.80683 0.90361 True -ADARB1_m46595675 ADARB1 568.84/470.63 704.7/398.81 519.73 551.76 0.086102 25804 16734 0.24756 0.59775 0.40225 0.8045 0.90303 True -AEBP1_m44144302 AEBP1 590.89/848.9 495.88/861.13 719.9 678.51 -0.085304 49995 28351 0.24583 0.40291 0.59709 0.80582 0.90349 False -AGTPBP1_m88296222 AGTPBP1 1672.7/903.96 1204.4/1508.8 1288.3 1356.6 0.074437 1.7092e+05 79307 0.24242 0.59577 0.40423 0.80845 0.90386 True -AEN_p89169534 AEN 1030.4/1065.6 1486.4/452.02 1048 969.2 -0.11263 2.6778e+05 1.0701e+05 0.24185 0.40445 0.59555 0.80889 0.90386 False -AGPAT5_m6566254 AGPAT5 764.33/475.95 369.7/785.6 620.14 577.65 -0.10223 64035 31244 0.24073 0.40488 0.59512 0.80976 0.90386 False -ADAD1_p123301373 ADAD1 923.08/836.47 918.58/770.16 879.78 844.37 -0.059198 7382.3 21912 0.23921 0.40547 0.59453 0.81094 0.90416 False -ADH5_m100009841 ADH5 1943.2/2260.8 2097.1/1992.9 2102 2045 -0.039631 27932 57767 0.23708 0.4063 0.5937 0.81259 0.90499 False -ADRB3_m37823842 ADRB3 142.58/0 0/138.47 71.289 69.234 -0.041603 9875.4 4185 0.23486 0.40716 0.59284 0.81431 0.90538 False -AFF2_p147891422 AFF2 122/626.91 374.12/303.26 374.45 338.69 -0.14443 64989 27310 0.23431 0.40737 0.59263 0.81475 0.90538 False -ADAP1_m994101 ADAP1 1148/797.4 909.11/1116.3 972.68 1012.7 0.058131 41459 30154 0.23055 0.59117 0.40883 0.81767 0.90609 True -ADH7_p100350724 ADH7 342.48/385.38 449.19/320.42 363.93 384.81 0.08026 4605.7 8206.9 0.23045 0.59112 0.40888 0.81776 0.90609 True -ACVR1C_p158443786 ACVR1C 1409.6/918.16 1267.5/1152.9 1163.9 1210.2 0.056251 63657 41164 0.22828 0.59029 0.40971 0.81943 0.90609 True -ABI1_p27149695 ABI1 232.24/76.366 152.68/185.39 154.3 169.03 0.13072 6341.7 4221 0.2267 0.58604 0.41396 0.82792 0.9069 True -ADCK2_m140373231 ADCK2 1314.1/1149 1178.5/1203.9 1231.6 1191.2 -0.048039 6969.3 31860 0.22615 0.41054 0.58946 0.82109 0.90609 False -ACAD11_p132378525 ACAD11 818.72/1150.8 718.58/1158.7 984.77 938.63 -0.06916 75990 41891 0.22544 0.41082 0.58918 0.82164 0.90609 False -ADARB1_m46595697 ADARB1 712.89/566.53 600.61/623.11 639.71 611.86 -0.064117 5481.9 15371 0.22465 0.41113 0.58887 0.82226 0.90609 False -ADAMTS5_p28338607 ADAMTS5 1114.2/941.25 1267.5/690.05 1027.7 978.75 -0.070341 90824 47641 0.22428 0.41127 0.58873 0.82254 0.90609 False -ADNP_m49518587 ADNP 3017.6/2642.6 1834.6/3573.3 2830.1 2703.9 -0.065775 7.9088e+05 3.1726e+05 0.22401 0.41137 0.58863 0.82275 0.90609 False -ACAD9_p128598525 ACAD9 574.72/531.01 271.28/929.22 552.86 600.25 0.11844 1.087e+05 44945 0.22353 0.58655 0.41345 0.82689 0.9069 True -ABL2_m179100544 ABL2 649.68/268.17 547.61/301.54 458.93 424.58 -0.11198 51526 24257 0.22298 0.41178 0.58822 0.82355 0.90609 False -ADH7_p100349757 ADH7 605.59/575.41 513.54/719.23 590.5 616.39 0.061807 10805 14061 0.21835 0.58642 0.41358 0.82716 0.9069 True -AFF3_p100623814 AFF3 949.54/1074.4 610.7/1307.4 1012 959.07 -0.077415 1.2526e+05 58824 0.21824 0.41362 0.58638 0.82725 0.9069 False -ACTR5_p37377163 ACTR5 532.09/822.26 991.13/451.45 677.18 721.29 0.090912 93862 42205 0.21472 0.5848 0.4152 0.83039 0.90861 True -ABCB8_p150730705 ABCB8 1092.1/927.04 883.25/1204.4 1009.6 1043.8 0.048104 32604 27893 0.20516 0.58128 0.41872 0.83745 0.91533 True -ADRB1_m115803996 ADRB1 645.27/515.02 705.97/400.53 580.15 553.25 -0.068381 27564 18379 0.19845 0.42134 0.57866 0.84269 0.92005 False -AHCTF1_m247067252 AHCTF1 196.96/301.91 423.96/34.331 249.44 229.14 -0.12191 40706 17163 0.19677 0.422 0.578 0.84401 0.92049 False -ACTR3_p114684953 ACTR3 85.253/635.79 181.07/624.25 360.52 402.66 0.15905 1.2488e+05 47040 0.19428 0.55559 0.44441 0.88882 0.94234 True -ADRM1_p60878687 ADRM1 202.84/319.67 429.01/51.496 261.26 240.25 -0.12044 39040 16798 0.19427 0.42298 0.57702 0.84596 0.92161 False -ABLIM2_p8108331 ABLIM2 1300.8/1157.9 1311.6/1078.6 1229.4 1195.1 -0.040773 18685 31797 0.19227 0.42376 0.57624 0.84753 0.92205 False -ACTL7A_m111624726 ACTL7A 405.68/303.69 417.02/257.48 354.69 337.25 -0.072514 8963.8 8304.8 0.1914 0.42411 0.57589 0.84821 0.92205 False -AFF1_m87967382 AFF1 371.88/348.09 144.47/526.41 359.98 335.44 -0.10157 36610 17608 0.18984 0.42472 0.57528 0.84944 0.92238 False -ACTR8_m53916111 ACTR8 651.15/1081.6 576/1072.3 866.35 824.14 -0.071988 1.0788e+05 50320 0.18828 0.42533 0.57467 0.85066 0.9227 False -ACD_p67694362 ACD 1590.4/1097.5 1333.1/1433.3 1344 1383.2 0.041472 63241 44489 0.18596 0.57376 0.42624 0.85247 0.92307 True -ADRA1B_m159344028 ADRA1B 1074.5/527.46 930.56/746.12 800.97 838.34 0.06572 83312 40930 0.18475 0.57327 0.42673 0.85346 0.92307 True -ADNP2_p77875445 ADNP2 655.56/619.81 562.12/667.74 637.68 614.93 -0.052338 3108.2 15316 0.18386 0.42706 0.57294 0.85412 0.92307 False -AGPAT5_m6566223 AGPAT5 1149.4/2033.5 1704/1361.2 1591.4 1532.6 -0.054301 2.2475e+05 1.0317e+05 0.18313 0.42735 0.57265 0.85469 0.92307 False -ADCK2_m140373168 ADCK2 689.37/548.77 447.93/740.97 619.07 594.45 -0.05844 26411 18683 0.18009 0.42854 0.57146 0.85708 0.92465 False -ADCK1_p78285386 ADCK1 809.9/687.29 678.84/865.71 748.6 772.27 0.044868 12489 18308 0.175 0.56946 0.43054 0.86108 0.92796 True -AEBP1_m44144338 AEBP1 580.6/351.64 693.35/293.53 466.12 493.44 0.082003 53070 24895 0.17315 0.56806 0.43194 0.86389 0.92909 True -ADD1_p2877761 ADD1 1411.1/648.22 1070/1076.8 1029.6 1073.4 0.060002 1.455e+05 65902 0.1705 0.56768 0.43232 0.86465 0.92909 True -ADCY1_p45614255 ADCY1 451.25/825.82 820.16/515.54 638.53 667.85 0.064655 58273 29651 0.17023 0.56754 0.43246 0.86492 0.92909 True -ACRC_p70800708 ACRC 2395.9/2441.9 2463/2461.5 2418.9 2462.3 0.025616 530.42 67543 0.1668 0.56624 0.43376 0.86753 0.93089 True -ADAD1_p123301349 ADAD1 956.89/285.93 505.97/664.87 621.41 585.42 -0.085913 1.1886e+05 49541 0.16543 0.4343 0.5657 0.86861 0.93105 False -ADRB2_p148206418 ADRB2 443.9/412.02 461.81/361.62 427.96 411.71 -0.055699 2763.7 9828.1 0.16388 0.43491 0.56509 0.86983 0.93136 False -AGAP2_p58129153 AGAP2 305.73/204.23 254.25/279.8 254.98 267.02 0.066304 2738.7 5525.1 0.16197 0.5642 0.4358 0.8716 0.93225 True -ACVR2A_m148602767 ACVR2A 1593.3/1419 1124.2/1805.2 1506.2 1464.7 -0.040206 1.2354e+05 67752 0.15914 0.43678 0.56322 0.87356 0.93336 False -ADAD1_m123301295 ADAD1 595.3/887.97 957.69/578.48 741.64 768.08 0.050485 57366 31201 0.14973 0.5595 0.4405 0.88099 0.94029 True -ACO2_m41895824 ACO2 590.89/333.88 292.1/679.75 462.38 485.93 0.071499 54082 25168 0.1484 0.5582 0.4418 0.8836 0.94106 True -ACTL7A_m111624641 ACTL7A 1931.4/1555.7 1583.5/1838.4 1743.6 1711 -0.027209 51526 48451 0.14808 0.44114 0.55886 0.88228 0.94066 False -ABL2_p179100489 ABL2 2303.3/1491.8 1720.4/1986.6 1897.5 1853.5 -0.033842 1.8234e+05 95146 0.1427 0.44326 0.55674 0.88653 0.94234 False -ADCK2_p140373158 ADCK2 837.83/953.68 861.16/972.71 895.76 916.94 0.03368 6466.2 22355 0.14167 0.55633 0.44367 0.88734 0.94234 True -ABL1_m133729594 ABL1 780.5/502.59 1136.9/78.389 641.55 607.63 -0.078246 2.994e+05 1.1008e+05 0.13952 0.44452 0.55548 0.88904 0.94234 False -ACVR1_m158637066 ACVR1 709.95/658.88 675.05/658.01 684.41 666.53 -0.038138 724.69 16570 0.13891 0.44476 0.55524 0.88952 0.94234 False -AHNAK_m62303523 AHNAK 179.32/298.36 150.15/305.54 238.84 227.85 -0.067691 9579.1 6618 0.13747 0.44533 0.55467 0.89066 0.94255 False -ACTN1_m69445724 ACTN1 1471.3/1317.8 1537.5/1199.9 1394.5 1368.7 -0.027001 34393 36587 0.13528 0.4462 0.5538 0.89239 0.94308 False -ADK_p76074481 ADK 438.02/74.59 254.88/231.16 256.31 243.02 -0.076482 33161 14758 0.13372 0.44681 0.55319 0.89362 0.94308 False -AAK1_m69870131 AAK1 1108.3/1355 890.19/1508.8 1231.7 1199.5 -0.038129 1.1091e+05 58211 0.13326 0.44699 0.55301 0.89399 0.94308 False -AFAP1L2_m116093045 AFAP1L2 708.48/847.13 752.65/767.87 777.8 760.26 -0.032869 4863.8 19105 0.12692 0.4495 0.5505 0.899 0.94737 False -ACAD11_p132378501 ACAD11 1065.7/742.35 903.43/865.71 904 884.57 -0.03131 26488 23886 0.12571 0.44998 0.55002 0.89996 0.94737 False -AHRR_p344030 AHRR 736.41/893.3 1114.8/459.46 814.85 787.12 -0.049891 1.1352e+05 51252 0.12272 0.45117 0.54883 0.90233 0.94887 False -AATK_p79104858 AATK 417.44/614.48 570.96/487.5 515.96 529.23 0.036555 11447 12101 0.1206 0.54799 0.45201 0.90401 0.94964 True -AAAS_m53715238 AAAS 47.036/239.75 265.6/44.058 143.39 154.83 0.10997 21556 9127.4 0.11971 0.51532 0.48468 0.96936 0.97895 True -ADRB2_p148206455 ADRB2 4950.5/3555.4 3992.9/4392.1 4253 4192.5 -0.020666 5.2639e+05 2.5987e+05 0.11869 0.45276 0.54724 0.90552 0.95023 False -AATF_p35307527 AATF 2185.7/1482.9 1741.9/1862.5 1834.3 1802.2 -0.025487 1.2711e+05 75463 0.11699 0.45343 0.54657 0.90687 0.95064 False -ADCK5_m145603170 ADCK5 858.4/802.73 1068.7/548.15 830.57 808.44 -0.03891 68525 36543 0.11576 0.45392 0.54608 0.90784 0.95066 False -ACAD11_m132378519 ACAD11 401.27/440.43 453.61/365.62 420.85 409.62 -0.038954 2318.8 9646.8 0.11443 0.45445 0.54555 0.9089 0.95077 False -ADRM1_m60878679 ADRM1 1584.5/1610.8 1700.9/1448.8 1597.7 1574.8 -0.020753 16063 42565 0.11067 0.45594 0.54406 0.91188 0.9529 False -ACVR1B_m52369226 ACVR1B 51.445/364.07 324.91/117.3 207.76 221.1 0.0894 35209 14669 0.11019 0.5233 0.4767 0.95339 0.97432 True -ADCK5_p145603142 ADCK5 1280.3/1479.4 1263/1536.3 1379.8 1399.7 0.020605 28579 36157 0.10446 0.5416 0.4584 0.9168 0.95704 True -ADRA1A_m26722473 ADRA1A 811.37/580.73 739.4/625.97 696.05 682.68 -0.027935 16515 16884 0.10287 0.45903 0.54097 0.91806 0.95726 False -ABL1_m133729467 ABL1 984.81/797.4 1328/394.23 891.11 861.13 -0.049315 2.2677e+05 90408 0.10177 0.45947 0.54053 0.91894 0.95726 False -ADARB2_m1779266 ADARB2 1568.4/2083.2 1810.7/1887.6 1825.8 1849.1 0.01834 67745 55503 0.099199 0.53951 0.46049 0.92098 0.95824 True -AEN_m89169495 AEN 662.91/594.94 834.04/391.94 628.93 612.99 -0.036969 50016 26727 0.097564 0.46114 0.53886 0.92228 0.95824 False -AGTPBP1_p88307644 AGTPBP1 302.79/335.65 274.44/347.89 319.22 311.16 -0.036787 1618.7 7093.6 0.095825 0.46183 0.53817 0.92366 0.95824 False -AGBL5_m27275975 AGBL5 779.03/932.37 778.52/905.19 855.7 841.85 -0.02351 9889.9 21246 0.095004 0.46216 0.53784 0.92431 0.95824 False -ABHD14B_m52004106 ABHD14B 446.84/394.26 519.22/340.45 420.55 429.84 0.03143 8681.2 9639 0.094568 0.53767 0.46233 0.92467 0.95824 True -ABTB1_m127395808 ABTB1 1184.7/650 888.29/910.91 917.36 899.6 -0.028163 71609 39174 0.089702 0.46426 0.53574 0.92852 0.96124 False -ADRB1_m115804041 ADRB1 414.5/1147.3 604.39/1002.5 780.88 803.43 0.041009 1.7385e+05 70742 0.084761 0.533 0.467 0.934 0.96456 True -ACO2_m41903807 ACO2 474.77/1126 864.95/773.02 800.36 818.98 0.033145 1.0812e+05 49189 0.083972 0.53339 0.46661 0.93322 0.96456 True -AFTPH_m64778724 AFTPH 49.976/127.87 105.99/66.945 88.922 86.467 -0.039928 1897.9 1774.5 0.081538 0.46751 0.53249 0.93501 0.96456 False -ADCK3_m227149144 ADCK3 263.11/168.72 189.27/231.73 215.91 210.5 -0.036444 2678.3 4592 0.080811 0.4678 0.5322 0.93559 0.96456 False -ABCF1_p30545888 ABCF1 264.58/353.41 442.25/191.68 309 316.97 0.036632 17670 10451 0.077983 0.53049 0.46951 0.93902 0.9661 True -ADD3_p111860411 ADD3 593.83/834.7 523.01/932.66 714.26 727.83 0.027114 56457 30403 0.077825 0.53101 0.46899 0.93799 0.96603 True -ADARB2_m1779272 ADARB2 1018.6/513.25 679.47/824.51 765.93 751.99 -0.026472 69110 35557 0.07398 0.47051 0.52949 0.94103 0.96717 False -ADRA1B_m159343921 ADRA1B 67.614/245.08 196.21/110.43 156.35 153.32 -0.028039 9713 5375.9 0.063038 0.47487 0.52513 0.94974 0.97432 False -ACTR3_p114688931 ACTR3 721.71/768.99 584.83/888.6 745.35 736.72 -0.016779 23627 20022 0.060988 0.47568 0.52432 0.95137 0.97432 False -AAAS_p53714391 AAAS 415.97/708.6 601.87/537.85 562.29 569.86 0.019263 22433 16355 0.059205 0.5236 0.4764 0.95279 0.97432 True -ADARB1_m46595715 ADARB1 749.63/378.28 472.54/673.46 563.96 573 0.022906 44569 23763 0.058653 0.52333 0.47667 0.95335 0.97432 True -ADI1_p3523171 ADI1 636.45/156.28 564.02/250.04 396.37 407.03 0.038194 82286 33445 0.058293 0.51593 0.48407 0.96814 0.97895 True -AGPAT3_m45379570 AGPAT3 127.88/230.87 191.16/160.78 179.38 175.97 -0.027492 2882.6 3736.7 0.057892 0.47692 0.52308 0.95383 0.97432 False -AHRR_p344042 AHRR 1230.3/525.68 837.19/945.24 877.98 891.22 0.021562 1.2703e+05 56920 0.055478 0.52207 0.47793 0.95587 0.9754 True -AEN_m89169500 AEN 679.08/568.3 741.3/492.65 623.69 616.97 -0.015604 18524 16137 0.052901 0.47891 0.52109 0.95781 0.976 False -ADCK4_m41220523 ADCK4 565.9/776.09 878.2/445.73 670.99 661.96 -0.01952 57802 30074 0.052147 0.47921 0.52079 0.95841 0.976 False -AEBP2_p19615544 AEBP2 745.22/751.23 736.25/773.59 748.23 754.92 0.012832 357.59 18298 0.049483 0.51973 0.48027 0.96053 0.97716 True -AEBP1_p44144359 AEBP1 770.21/880.87 577.9/1056.8 825.54 817.36 -0.014356 60403 33744 0.044558 0.48223 0.51777 0.96446 0.97895 False -AHCTF1_p247079429 AHCTF1 601.18/900.41 735.62/754.13 750.79 744.88 -0.011396 22470 19735 0.042105 0.48321 0.51679 0.96641 0.97895 False -ADNP2_p77875499 ADNP2 745.22/614.48 453.61/920.64 679.85 687.12 0.015331 58803 30566 0.041603 0.51657 0.48343 0.96686 0.97895 True -ACTR5_m37377171 ACTR5 438.02/443.99 482/391.94 441 436.97 -0.013222 2036.4 10162 0.040007 0.48404 0.51596 0.96809 0.97895 False -ABTB1_m127395860 ABTB1 945.13/1102.9 885.77/1174.7 1024 1030.2 0.0087472 27089 26326 0.038419 0.51532 0.48468 0.96935 0.97895 True -ACTL6B_m100253168 ACTL6B 751.1/939.48 471.91/1238.2 845.29 855.05 0.016548 1.5567e+05 65863 0.038041 0.51493 0.48507 0.97013 0.97895 True -AEBP2_m19615572 AEBP2 1023/468.85 729.31/774.73 745.94 752.02 0.011697 77295 37922 0.031226 0.51242 0.48758 0.97515 0.98302 True -AFF1_p87967890 AFF1 824.6/1007 526.79/1316.6 915.78 921.69 0.0092723 1.6426e+05 70028 0.022338 0.50878 0.49122 0.98244 0.98938 True -ACHE_p100491656 ACHE 2626.7/1706.7 1749.5/2570.2 2166.7 2159.8 -0.0045493 3.8001e+05 1.665e+05 0.016725 0.49333 0.50667 0.98666 0.9925 False -ACHE_m100491816 ACHE 295.44/420.9 380.43/338.73 358.17 359.58 0.005641 4369.4 8062.6 0.015671 0.50624 0.49376 0.98753 0.9925 True -ACHE_m100491779 ACHE 280.75/525.68 298.41/505.24 403.21 401.82 -0.0049676 25692 14696 0.012018 0.49521 0.50479 0.99041 0.99439 False -AGAP2_m58128444 AGAP2 2459.1/1609 1964.6/2109.6 2034.1 2037.1 0.0021679 1.8592e+05 99102 0.0097214 0.50388 0.49612 0.99224 0.99523 True -ADRB2_p148206450 ADRB2 608.53/877.32 955.17/533.27 742.92 744.22 0.0025145 62561 32956 0.0071485 0.50284 0.49716 0.99432 0.99631 True -ACVRL1_m52306881 ACVRL1 1665.4/1102.9 1593/1178.1 1384.1 1385.6 0.0015047 1.2213e+05 64899 0.0056737 0.50226 0.49774 0.99547 0.99647 True -ACAD11_m132378564 ACAD11 989.22/1545.1 1045.4/1487.1 1267.1 1266.2 -0.0010322 1.2602e+05 63931 0.0035873 0.49857 0.50143 0.99714 0.99714 False
--- a/test-data/output.count_normalized.txt Thu Apr 19 05:35:12 2018 -0400 +++ b/test-data/output.count_normalized.txt Mon Jun 04 10:58:16 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 2.0 s_24835 HCFC1R1 2.0 s_14784 CYP4B1 8.0
--- a/test-data/output_summary.Rnw Thu Apr 19 05:35:12 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1063 +0,0 @@ -% This is a template file for Sweave used in MAGeCK -% Author: Wei Li, Shirley Liu lab -% Do not modify lines beginning with "#__". -\documentclass{article} - -\usepackage{amsmath} -\usepackage{amscd} -\usepackage[tableposition=top]{caption} -\usepackage{ifthen} -\usepackage{fullpage} -\usepackage[utf8]{inputenc} - -\begin{document} -\setkeys{Gin}{width=0.9\textwidth} - -\title{MAGeCK Comparison Report} -\author{Wei Li} - -\maketitle - - -\tableofcontents - -\section{Summary} - -%Function definition -<<label=funcdef,include=FALSE,echo=FALSE>>= -genreporttable<-function(comparisons,ngenes,direction,fdr1,fdr5,fdr25){ - xtb=data.frame(Comparison=comparisons,Genes=ngenes,Selection=direction,FDR1=fdr1,FDR5=fdr5,FDR25=fdr25); - colnames(xtb)=c("Comparison","Genes","Selection","FDR1%","FDR5%","FDR25%"); - return (xtb); -} -@ - -The statistics of comparisons is as indicated in the following table. - -<<label=tab1,echo=FALSE,results=tex>>= -library(xtable) -comparisons=c("HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.","HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos."); -ngenes=c(100,100); -direction=c("negative","positive"); -fdr1=c(0,0); -fdr5=c(2,0); -fdr25=c(9,1); - -cptable=genreporttable(comparisons,ngenes,direction,fdr1,fdr5,fdr25); -print(xtable(cptable, caption = "Summary of comparisons", label = "tab:one", - digits = c(0, 0, 0, 0, 0, 0, 0), - table.placement = "tbp", - caption.placement = "top")) -@ - -The meanings of the columns are as follows. - -\begin{itemize} -\item \textbf{Comparison}: The label for comparisons; -\item \textbf{Genes}: The number of genes in the library; -\item \textbf{Selection}: The direction of selection, either positive selection or negative selection; -\item \textbf{FDR1\%}: The number of genes with FDR $<$ 1\%; -\item \textbf{FDR5\%}: The number of genes with FDR $<$ 5\%; -\item \textbf{FDR25\%}: The number of genes with FDR $<$ 25\%; -\end{itemize} - -The following figures show: - -\begin{itemize} -\item Individual sgRNA read counts of selected genes in selected samples; -\item The distribution of RRA scores and p values of all genes; and -\item The RRA scores and p values of selected genes. -\end{itemize} - - -\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg.} - -The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg., and the RRA scores of 10 genes. - -<<echo=FALSE>>= -gstable=read.table('output.gene_summary.txt',header=T) -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=3 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1") -# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - -@ -%% -\clearpage -\newpage -The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples. -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285)) -targetgene="ACIN1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424)) -targetgene="ACTR8" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344)) -targetgene="AHCY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021)) -targetgene="ACLY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173)) -targetgene="AATF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033)) -targetgene="AGBL5" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991)) -targetgene="AHCTF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427)) -targetgene="ABT1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338)) -targetgene="ADIRF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927)) -targetgene="ABCF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ - -\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos.} - -The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos., and the RRA scores of 10 genes. - -<<echo=FALSE>>= -gstable=read.table('output.gene_summary.txt',header=T) -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=9 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP") -# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - -@ -%% -\clearpage -\newpage -The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples. -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192)) -targetgene="ACRC" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882)) -targetgene="AGAP3" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471)) -targetgene="ADCK4" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885)) -targetgene="AHRR" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979)) -targetgene="ADRBK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076)) -targetgene="ADK" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816)) -targetgene="ADCK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512)) -targetgene="ADARB2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832)) -targetgene="ACSS2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625)) -targetgene="ADNP" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -%__INDIVIDUAL_PAGE__ - - - - - - - - - -\end{document} -