| Previous changeset 3:f259c29b3832 (2018-04-19) Next changeset 5:d46e36492bff (2019-01-05) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mageck commit 4478aabdcb10e4787450b1b23944defa7dc38ffe |
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modified:
mageck_macros.xml 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/output.count_normalized.txt |
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added:
test-data/in.mle.sgrnaeff |
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removed:
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_summary.Rnw |
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| diff -r f259c29b3832 -r 3b22ff50a362 mageck_macros.xml --- a/mageck_macros.xml Thu Apr 19 05:34:15 2018 -0400 +++ b/mageck_macros.xml Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -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> |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/in.mle.sgrnaeff --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in.mle.sgrnaeff Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -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 |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.count.bam.txt --- a/test-data/out.count.bam.txt Thu Apr 19 05:34:15 2018 -0400 +++ b/test-data/out.count.bam.txt Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -1,4 +1,4 @@ -sgRNA Gene test1_bam +sgRNA Gene test1.bam s_10007 CCNA1 0 s_10008 CCNA1 0 s_10027 CCNC 0 |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.count.fastq.txt --- a/test-data/out.count.fastq.txt Thu Apr 19 05:34:15 2018 -0400 +++ b/test-data/out.count.fastq.txt Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -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 |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.count.txt --- a/test-data/out.count.txt Thu Apr 19 05:34:15 2018 -0400 +++ b/test-data/out.count.txt Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -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 |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.count_multi.txt --- a/test-data/out.count_multi.txt Thu Apr 19 05:34:15 2018 -0400 +++ b/test-data/out.count_multi.txt Mon Jun 04 10:57:38 2018 -0400 |
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| @@ -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 |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.test.R --- a/test-data/out.test.R Thu Apr 19 05:34:15 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,930 +0,0 @@\n-pdf(file=\'output.pdf\',width=4.5,height=4.5);\n-gstable=read.table(\'output.gene_summary.txt\',header=T)\n-# \n-#\n-# parameters\n-# Do not modify the variables beginning with "__"\n-\n-# gstablename=\'__GENE_SUMMARY_FILE__\'\n-startindex=3\n-# outputfile=\'__OUTPUT_FILE__\'\n-targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1")\n-# samplelabel=sub(\'.\\\\w+.\\\\w+$\',\'\',colnames(gstable)[startindex]);\n-samplelabel=\'HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.\'\n-\n-\n-# You need to write some codes in front of this code:\n-# gstable=read.table(gstablename,header=T)\n-# pdf(file=outputfile,width=6,height=6)\n-\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-######\n-# function definition\n-\n-plotrankedvalues<-function(val, tglist, ...){\n- \n- plot(val,log=\'y\',ylim=c(max(val),min(val)),type=\'l\',lwd=2, ...)\n- if(length(tglist)>0){\n- for(i in 1:length(tglist)){\n- targetgene=tglist[i];\n- tx=which(names(val)==targetgene);ty=val[targetgene];\n- points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)\n- # text(tx+50,ty,targetgene,col=colors[i])\n- }\n- legend(\'topright\',tglist,pch=20,pt.cex = 2,cex=1,col=colors)\n- }\n-}\n-\n-\n-\n-plotrandvalues<-function(val,targetgenelist, ...){\n- # choose the one with the best distance distribution\n- \n- mindiffvalue=0;\n- randval=val;\n- for(i in 1:20){\n- randval0=sample(val)\n- vindex=sort(which(names(randval0) %in% targetgenelist))\n- if(max(vindex)>0.9*length(val)){\n- # print(\'pass...\')\n- next;\n- }\n- mindiffind=min(diff(vindex));\n- if (mindiffind > mindiffvalue){\n- mindiffvalue=mindiffind;\n- randval=randval0;\n- # print(paste(\'Diff: \',mindiffvalue))\n- }\n- }\n- plot(randval,log=\'y\',ylim=c(max(randval),min(randval)),pch=20,col=\'grey\', ...)\n- \n- if(length(targetgenelist)>0){\n- for(i in 1:length(targetgenelist)){\n- targetgene=targetgenelist[i];\n- tx=which(names(randval)==targetgene);ty=randval[targetgene];\n- points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)\n- text(tx+50,ty,targetgene,col=colors[i])\n- }\n- }\n- \n-}\n-\n-\n-\n-\n-# set.seed(1235)\n-\n-\n-\n-pvec=gstable[,startindex]\n-names(pvec)=gstable[,\'id\']\n-pvec=sort(pvec);\n-\n-plotrankedvalues(pvec,targetgenelist,xlab=\'Genes\',ylab=\'RRA score\',main=paste(\'Distribution of RRA scores in \\\\n\',samplelabel))\n-\n-# plotrandvalues(pvec,targetgenelist,xlab=\'Genes\',ylab=\'RRA score\',main=paste(\'Distribution of RRA scores in \\\\n\',samplelabel))\n-\n-\n-pvec=gstable[,startindex+1]\n-names(pvec)=gstable[,\'id\']\n-pvec=sort(pvec);\n-\n-plotrankedvalues(pvec,targetgenelist,xlab=\'Genes\',ylab=\'p value\',main=paste(\'Distribution of p values in \\\\n\',samplelabel))\n-\n-# plotrandvalues(pvec,targetgenelist,xlab=\'Genes\',ylab=\'p value\',main=paste(\'Distribution of p values in \\\\n\',samplelabel))\n-\n-\n-\n-# you need to write after this code:\n-# dev.off()\n-\n-\n-\n-\n-\n-\n-# parameters\n-# Do not modify the variables beginning with "__"\n-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(946.5969148654764,470.6260845366416,663.0651476194316,45'..b'2554626,591.3905137762326,1061.787481868224,887.4532212761591),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))\n-targetgene="ACSS2"\n-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-\n-## code\n-\n-targetmatvec=unlist(targetmat)+1\n-yrange=range(targetmatvec[targetmatvec>0]);\n-# yrange[1]=1; # set the minimum value to 1\n-for(i in 1:length(targetmat)){\n- vali=targetmat[[i]]+1;\n- if(i==1){\n- 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\')\n- axis(1,at=1:length(vali),labels=(collabel),las=2)\n- # lines(0:100,rep(1,101),col=\'black\');\n- }else{\n- lines(1:length(vali),vali,type=\'b\',pch=20,col=colors[(i %% length(colors))])\n- }\n-}\n-\n-\n-\n-\n-# parameters\n-# Do not modify the variables beginning with "__"\n-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))\n-targetgene="ADNP"\n-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-\n-## code\n-\n-targetmatvec=unlist(targetmat)+1\n-yrange=range(targetmatvec[targetmatvec>0]);\n-# yrange[1]=1; # set the minimum value to 1\n-for(i in 1:length(targetmat)){\n- vali=targetmat[[i]]+1;\n- if(i==1){\n- 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\')\n- axis(1,at=1:length(vali),labels=(collabel),las=2)\n- # lines(0:100,rep(1,101),col=\'black\');\n- }else{\n- lines(1:length(vali),vali,type=\'b\',pch=20,col=colors[(i %% length(colors))])\n- }\n-}\n-\n-\n-\n-dev.off()\n-Sweave("output_summary.Rnw");\n-library(tools);\n-\n-texi2dvi("output_summary.tex",pdf=TRUE);\n-\n' |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.test.log.txt --- a/test-data/out.test.log.txt Thu Apr 19 05:34:15 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| b'@@ -1,109 +0,0 @@\n-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 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to MAGeCK v0.5.7. Command: test \n-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 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Processing 1 lines.. \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Parsing error in line 1 (usually the header line). Skip this line. \n-INFO @ Mon, 26 Mar 2018 08:37:53: Loaded 999 records. \n-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. \n-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. \n-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. \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Setting up the visualization module... \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_final,KBM7_final \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 2 3 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Treatment samples:HL60_final,KBM7_final \n-INFO @ Mon, 26 Mar 2018 08:37:53: Treatment sample index:2,3 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_initial,KBM7_initial \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 0 1 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Control samples:HL60_initial,KBM7_initial \n-INFO @ Mon, 26 Mar 2018 08:37:53: Control sample index:0,1 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Initial (total) size factor: 1.6666455325878438 2.027372749328462 0.7198064117880387 0.6589869375844738 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Median factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Final size factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Writing normalized read counts to output.normalized.txt \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Adjusted model: 1.1175084644498339\t3.4299551007579927 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Raw variance calculation: 0.5 for control, 0.5 for treatment \n-INFO @ Mon, 26 Mar 2018 08:37:53: Adjusted variance calculation: 0.3333333333333333 for raw variance, 0.6666666666666667 for modeling \n-INFO @ Mon, 26 Mar 2018 08:37:53: Use qnorm to reversely calculate sgRNA scores ... \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: lower test FDR cutoff: 0.3283283283283283 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: higher test FDR cutoff: 0.34534534534534533 \n-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 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to RRA v 0.5.7. \n-INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene NA for permutation ... \n-INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene na for permutation ... \n-INFO @ Mon, 26 Mar 2018 08:37:53: Reading input file... \n-INFO @ Mon, 26 Mar 2018 08:37:53: Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Computing lo-values for each group... \n-I'..b'-INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 9 sgRNAs... \n-INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 10 sgRNAs... \n-INFO @ Mon, 26 Mar 2018 08:37:53: Number of genes under FDR adjustment: 100 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Saving to output file... \n-INFO @ Mon, 26 Mar 2018 08:37:53: RRA completed. \n-INFO @ Mon, 26 Mar 2018 08:37:53: \n-INFO @ Mon, 26 Mar 2018 08:37:53: End command message. \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Sorting the merged items by negative selection... \n-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 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:3 \n-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 \n-DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:9 \n-INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.plow.txt \n-INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.phigh.txt \n-INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.low.txt \n-INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.high.txt \n-INFO @ Mon, 26 Mar 2018 08:37:54: Running command: cd ./; Rscript output.R \n-INFO @ Mon, 26 Mar 2018 08:37:59: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:59: null device \n-INFO @ Mon, 26 Mar 2018 08:37:59: 1 \n-INFO @ Mon, 26 Mar 2018 08:37:59: Writing to file output_summary.tex \n-INFO @ Mon, 26 Mar 2018 08:37:59: Processing code chunks with options ... \n-INFO @ Mon, 26 Mar 2018 08:37:59: 1 : keep.source term verbatim (label = funcdef, output_summary.Rnw:27) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 2 : keep.source term tex (label = tab1, output_summary.Rnw:37) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 3 : keep.source term verbatim (output_summary.Rnw:77) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 4 : keep.source term verbatim pdf (output_summary.Rnw:83) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 5 : keep.source term verbatim pdf (output_summary.Rnw:201) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 6 : keep.source term verbatim pdf (output_summary.Rnw:345) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 7 : keep.source term verbatim pdf (output_summary.Rnw:489) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 8 : keep.source term verbatim (output_summary.Rnw:567) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 9 : keep.source term verbatim pdf (output_summary.Rnw:573) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 10 : keep.source term verbatim pdf (output_summary.Rnw:691) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 11 : keep.source term verbatim pdf (output_summary.Rnw:835) \n-INFO @ Mon, 26 Mar 2018 08:37:59: 12 : keep.source term verbatim pdf (output_summary.Rnw:979) \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: You can now run (pdf)latex on \xe2\x80\x98output_summary.tex\xe2\x80\x99 \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: End command message. \n-INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary-*.pdf \n-INFO @ Mon, 26 Mar 2018 08:37:59: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: End command message. \n-INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.aux \n-INFO @ Mon, 26 Mar 2018 08:37:59: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: End command message. \n-INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.tex \n-INFO @ Mon, 26 Mar 2018 08:37:59: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: End command message. \n-INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.toc \n-INFO @ Mon, 26 Mar 2018 08:37:59: Command message: \n-INFO @ Mon, 26 Mar 2018 08:37:59: \n-INFO @ Mon, 26 Mar 2018 08:37:59: End command message. \n' |
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| diff -r f259c29b3832 -r 3b22ff50a362 test-data/out.test.sgrna_summary.txt --- a/test-data/out.test.sgrna_summary.txt Thu Apr 19 05:34:15 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| b'@@ -1,1000 +0,0 @@\n-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\n-AHRR_p344008\tAHRR\t251.35/221.99\t1564.6/1502.5\t236.67\t1533.6\t2.6908\t1178.2\t5085.6\t18.186\t1\t3.3319e-74\t6.6638e-74\t6.6571e-71\tTrue\n-ACAD9_m128598565\tACAD9\t739.35/925.27\t2528/2050.7\t832.31\t2289.3\t1.4586\t65590\t35597\t7.7226\t1\t5.7002e-15\t1.14e-14\t5.6945e-12\tTrue\n-ACTR8_m53916081\tACTR8\t1925.5/2054.8\t194.94/235.17\t1990.2\t215.06\t-3.2041\t4580.2\t54357\t7.6137\t1.3311e-14\t1\t2.6622e-14\t7.8608e-12\tFalse\n-ACRC_m70814198\tACRC\t76.433/90.573\t447.3/357.04\t83.503\t402.17\t2.2543\t2086.7\t1760.4\t7.5952\t1\t1.5737e-14\t3.1475e-14\t7.8608e-12\tTrue\n-ABCC1_p16101710\tABCC1\t52.915/26.639\t203.78/224.3\t39.777\t214.04\t2.3987\t277.85\t700.66\t6.5833\t1\t2.465e-11\t4.93e-11\t9.8502e-09\tTrue\n-ACTR8_m53916067\tACTR8\t1267/1156.1\t251.09/42.341\t1211.6\t146.72\t-3.0372\t13968\t31286\t6.021\t8.6646e-10\t1\t1.7329e-09\t2.3419e-07\tFalse\n-AAK1_m69870125\tAAK1\t402.74/621.58\t1149.5/1202.2\t512.16\t1175.8\t1.1974\t12666\t12223\t6.0027\t1\t9.7009e-10\t1.9402e-09\t2.3419e-07\tTrue\n-ADCK1_p78285331\tADCK1\t798.14/768.99\t1478.8/1756\t783.56\t1617.4\t1.0446\t19425\t19317\t5.9996\t1\t9.8882e-10\t1.9776e-09\t2.3419e-07\tTrue\n-AHCY_m32883247\tAHCY\t1142.1/1099.3\t112.93/100.7\t1120.7\t106.82\t-3.379\t494.86\t28685\t5.9891\t1.0549e-09\t1\t2.1098e-09\t2.3419e-07\tFalse\n-AHCTF1_m247070995\tAHCTF1\t1437.5/1095.8\t320.49/161.36\t1266.6\t240.92\t-2.3895\t35534\t33759\t5.5826\t1.1847e-08\t1\t2.3695e-08\t2.3671e-06\tFalse\n-AHCY_p32883309\tAHCY\t1053.9/882.65\t106.62/105.85\t968.27\t106.24\t-3.1761\t7331.9\t24378\t5.524\t1.6565e-08\t1\t3.3129e-08\t3.0087e-06\tFalse\n-ADCY1_p45614315\tADCY1\t2.9397/72.814\t210.72/187.1\t37.877\t198.91\t2.3624\t1360\t895.72\t5.3806\t1\t4.1374e-08\t8.2747e-08\t6.6138e-06\tTrue\n-ACTN4_p39138476\tACTN4\t449.78/475.95\t1056.7/977.86\t462.87\t1017.3\t1.1344\t1726.9\t10724\t5.3539\t1\t4.3033e-08\t8.6065e-08\t6.6138e-06\tTrue\n-ADAM12_m128076658\tADAM12\t768.74/767.21\t1432.1/1562.6\t767.98\t1497.4\t0.96239\t4258.6\t18836\t5.3145\t1\t5.3464e-08\t1.0693e-07\t7.63e-06\tTrue\n-ABT1_m26597388\tABT1\t1743.3/1980.2\t837.19/281.51\t1861.7\t559.35\t-1.733\t91226\t64053\t5.1459\t1.3309e-07\t1\t2.6618e-07\t1.7727e-05\tFalse\n-ACRC_p70814182\tACRC\t682.02/822.26\t1572.2/1333.8\t752.14\t1453\t0.949\t19128\t18646\t5.1324\t1\t1.4307e-07\t2.8614e-07\t1.7866e-05\tTrue\n-ACBD6_p180471256\tACBD6\t107.3/285.93\t553.29/687.76\t196.61\t620.53\t1.6531\t12498\t6924.5\t5.0942\t1\t1.7666e-07\t3.5333e-07\t2.0382e-05\tTrue\n-ACRC_p70811990\tACRC\t495.35/605.6\t1159.6/1617.6\t550.47\t1388.6\t1.3333\t55476\t27162\t5.0853\t1\t1.8362e-07\t3.6724e-07\t2.0382e-05\tTrue\n-ADRA1B_m159344001\tADRA1B\t329.25/309.01\t881.98/676.32\t319.13\t779.15\t1.2851\t10677\t8286.5\t5.0535\t1\t2.1699e-07\t4.3398e-07\t2.2818e-05\tTrue\n-AHCY_p32883238\tAHCY\t61.735/218.44\t614.49/452.6\t140.09\t533.54\t1.9217\t12691\t6123\t5.0282\t1\t2.5699e-07\t5.1398e-07\t2.5673e-05\tTrue\n-ACTR5_m37377141\tACTR5\t865.75/935.92\t1595.5/1695.4\t900.84\t1645.4\t0.86841\t3723.6\t22496\t4.9645\t1\t3.4447e-07\t6.8893e-07\t3.2774e-05\tTrue\n-ADARB2_m1779330\tADARB2\t135.23/118.99\t351.41/399.95\t127.11\t375.68\t1.556\t655.19\t2547.9\t4.9245\t1\t4.2536e-07\t8.5071e-07\t3.863e-05\tTrue\n-ADRB1_m115804012\tADRB1\t2166.6/1942.9\t1093.3/705.5\t2054.7\t899.42\t-1.191\t50114\t56324\t4.8681\t5.6346e-07\t1\t1.1269e-06\t4.8948e-05\tFalse\n-AHCTF1_m247070906\tAHCTF1\t1078.9/1609\t348.88/193.97\t1343.9\t271.43\t-2.3036\t76257\t48827\t4.8539\t6.0526e-07\t1\t1.2105e-06\t5.0388e-05\tFalse\n-ACIN1_m23538803\tACIN1\t2432.6/2122.3\t1067.5/155.63\t2277.4\t611.55\t-1.8952\t2.3194e+05\t1.1942e+05\t4.8207\t7.1537e-07\t1\t1.4307e-06\t5.7173e-05\tFalse\n-ACIN1_m23538698\tACIN1\t3424.8/3818.3\t1992.3/690.05\t3621.5\t1341.2\t-1.4324\t4.627e+05\t2.2481e+05\t4.8094\t7.5677e-07\t1\t1.5135e-06\t5.8155e-05\tFalse\n-ABCF1_p30545878\tABCF1\t2012.3/1989.1\t1064.3/432\t2000.7\t748.15\t-1.4179\t1.0009e+05\t69814\t4.7403\t1.0669e-06\t1\t2.1338e-06\t7.8951e-05\tFalse\n-ABCF1_p30539251\tABCF1\t1127.4/1198.8\t371.59/370.2\t1163.1\t370.9\t-1.6462\t1274\t29895\t4.5817\t2.3062e-06\t1\t4.6124e-06\t0.00016456\tFalse\n-ACTR1A_m104250365\tACTR1A\t402.74/161.61\t711.64/716.37\t282.18\t714.01\t1.3362\t14542\t8970.1\t4.5595\t1\t2.5679e-06\t5.1359e-06\t0.00017692\tTrue\n-ADRBK1_m6703'..b'PH_m64778724\tAFTPH\t49.976/127.87\t105.99/66.945\t88.922\t86.467\t-0.039928\t1897.9\t1774.5\t0.081538\t0.46751\t0.53249\t0.93501\t0.96456\tFalse\n-ADCK3_m227149144\tADCK3\t263.11/168.72\t189.27/231.73\t215.91\t210.5\t-0.036444\t2678.3\t4592\t0.080811\t0.4678\t0.5322\t0.93559\t0.96456\tFalse\n-ABCF1_p30545888\tABCF1\t264.58/353.41\t442.25/191.68\t309\t316.97\t0.036632\t17670\t10451\t0.077983\t0.53049\t0.46951\t0.93902\t0.9661\tTrue\n-ADD3_p111860411\tADD3\t593.83/834.7\t523.01/932.66\t714.26\t727.83\t0.027114\t56457\t30403\t0.077825\t0.53101\t0.46899\t0.93799\t0.96603\tTrue\n-ADARB2_m1779272\tADARB2\t1018.6/513.25\t679.47/824.51\t765.93\t751.99\t-0.026472\t69110\t35557\t0.07398\t0.47051\t0.52949\t0.94103\t0.96717\tFalse\n-ADRA1B_m159343921\tADRA1B\t67.614/245.08\t196.21/110.43\t156.35\t153.32\t-0.028039\t9713\t5375.9\t0.063038\t0.47487\t0.52513\t0.94974\t0.97432\tFalse\n-ACTR3_p114688931\tACTR3\t721.71/768.99\t584.83/888.6\t745.35\t736.72\t-0.016779\t23627\t20022\t0.060988\t0.47568\t0.52432\t0.95137\t0.97432\tFalse\n-AAAS_p53714391\tAAAS\t415.97/708.6\t601.87/537.85\t562.29\t569.86\t0.019263\t22433\t16355\t0.059205\t0.5236\t0.4764\t0.95279\t0.97432\tTrue\n-ADARB1_m46595715\tADARB1\t749.63/378.28\t472.54/673.46\t563.96\t573\t0.022906\t44569\t23763\t0.058653\t0.52333\t0.47667\t0.95335\t0.97432\tTrue\n-ADI1_p3523171\tADI1\t636.45/156.28\t564.02/250.04\t396.37\t407.03\t0.038194\t82286\t33445\t0.058293\t0.51593\t0.48407\t0.96814\t0.97895\tTrue\n-AGPAT3_m45379570\tAGPAT3\t127.88/230.87\t191.16/160.78\t179.38\t175.97\t-0.027492\t2882.6\t3736.7\t0.057892\t0.47692\t0.52308\t0.95383\t0.97432\tFalse\n-AHRR_p344042\tAHRR\t1230.3/525.68\t837.19/945.24\t877.98\t891.22\t0.021562\t1.2703e+05\t56920\t0.055478\t0.52207\t0.47793\t0.95587\t0.9754\tTrue\n-AEN_m89169500\tAEN\t679.08/568.3\t741.3/492.65\t623.69\t616.97\t-0.015604\t18524\t16137\t0.052901\t0.47891\t0.52109\t0.95781\t0.976\tFalse\n-ADCK4_m41220523\tADCK4\t565.9/776.09\t878.2/445.73\t670.99\t661.96\t-0.01952\t57802\t30074\t0.052147\t0.47921\t0.52079\t0.95841\t0.976\tFalse\n-AEBP2_p19615544\tAEBP2\t745.22/751.23\t736.25/773.59\t748.23\t754.92\t0.012832\t357.59\t18298\t0.049483\t0.51973\t0.48027\t0.96053\t0.97716\tTrue\n-AEBP1_p44144359\tAEBP1\t770.21/880.87\t577.9/1056.8\t825.54\t817.36\t-0.014356\t60403\t33744\t0.044558\t0.48223\t0.51777\t0.96446\t0.97895\tFalse\n-AHCTF1_p247079429\tAHCTF1\t601.18/900.41\t735.62/754.13\t750.79\t744.88\t-0.011396\t22470\t19735\t0.042105\t0.48321\t0.51679\t0.96641\t0.97895\tFalse\n-ADNP2_p77875499\tADNP2\t745.22/614.48\t453.61/920.64\t679.85\t687.12\t0.015331\t58803\t30566\t0.041603\t0.51657\t0.48343\t0.96686\t0.97895\tTrue\n-ACTR5_m37377171\tACTR5\t438.02/443.99\t482/391.94\t441\t436.97\t-0.013222\t2036.4\t10162\t0.040007\t0.48404\t0.51596\t0.96809\t0.97895\tFalse\n-ABTB1_m127395860\tABTB1\t945.13/1102.9\t885.77/1174.7\t1024\t1030.2\t0.0087472\t27089\t26326\t0.038419\t0.51532\t0.48468\t0.96935\t0.97895\tTrue\n-ACTL6B_m100253168\tACTL6B\t751.1/939.48\t471.91/1238.2\t845.29\t855.05\t0.016548\t1.5567e+05\t65863\t0.038041\t0.51493\t0.48507\t0.97013\t0.97895\tTrue\n-AEBP2_m19615572\tAEBP2\t1023/468.85\t729.31/774.73\t745.94\t752.02\t0.011697\t77295\t37922\t0.031226\t0.51242\t0.48758\t0.97515\t0.98302\tTrue\n-AFF1_p87967890\tAFF1\t824.6/1007\t526.79/1316.6\t915.78\t921.69\t0.0092723\t1.6426e+05\t70028\t0.022338\t0.50878\t0.49122\t0.98244\t0.98938\tTrue\n-ACHE_p100491656\tACHE\t2626.7/1706.7\t1749.5/2570.2\t2166.7\t2159.8\t-0.0045493\t3.8001e+05\t1.665e+05\t0.016725\t0.49333\t0.50667\t0.98666\t0.9925\tFalse\n-ACHE_m100491816\tACHE\t295.44/420.9\t380.43/338.73\t358.17\t359.58\t0.005641\t4369.4\t8062.6\t0.015671\t0.50624\t0.49376\t0.98753\t0.9925\tTrue\n-ACHE_m100491779\tACHE\t280.75/525.68\t298.41/505.24\t403.21\t401.82\t-0.0049676\t25692\t14696\t0.012018\t0.49521\t0.50479\t0.99041\t0.99439\tFalse\n-AGAP2_m58128444\tAGAP2\t2459.1/1609\t1964.6/2109.6\t2034.1\t2037.1\t0.0021679\t1.8592e+05\t99102\t0.0097214\t0.50388\t0.49612\t0.99224\t0.99523\tTrue\n-ADRB2_p148206450\tADRB2\t608.53/877.32\t955.17/533.27\t742.92\t744.22\t0.0025145\t62561\t32956\t0.0071485\t0.50284\t0.49716\t0.99432\t0.99631\tTrue\n-ACVRL1_m52306881\tACVRL1\t1665.4/1102.9\t1593/1178.1\t1384.1\t1385.6\t0.0015047\t1.2213e+05\t64899\t0.0056737\t0.50226\t0.49774\t0.99547\t0.99647\tTrue\n-ACAD11_m132378564\tACAD11\t989.22/1545.1\t1045.4/1487.1\t1267.1\t1266.2\t-0.0010322\t1.2602e+05\t63931\t0.0035873\t0.49857\t0.50143\t0.99714\t0.99714\tFalse\n' |
| b |
| diff -r f259c29b3832 -r 3b22ff50a362 test-data/output.count_normalized.txt --- a/test-data/output.count_normalized.txt Thu Apr 19 05:34:15 2018 -0400 +++ b/test-data/output.count_normalized.txt Mon Jun 04 10:57:38 2018 -0400 |
| b |
| @@ -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 |
| b |
| diff -r f259c29b3832 -r 3b22ff50a362 test-data/output_summary.Rnw --- a/test-data/output_summary.Rnw Thu Apr 19 05:34:15 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,1063 +0,0 @@\n-% This is a template file for Sweave used in MAGeCK\n-% Author: Wei Li, Shirley Liu lab\n-% Do not modify lines beginning with "#__".\n-\\documentclass{article}\n-\n-\\usepackage{amsmath}\n-\\usepackage{amscd}\n-\\usepackage[tableposition=top]{caption}\n-\\usepackage{ifthen}\n-\\usepackage{fullpage}\n-\\usepackage[utf8]{inputenc}\n-\n-\\begin{document}\n-\\setkeys{Gin}{width=0.9\\textwidth}\n-\n-\\title{MAGeCK Comparison Report}\n-\\author{Wei Li}\n-\n-\\maketitle\n-\n-\n-\\tableofcontents\n-\n-\\section{Summary}\n-\n-%Function definition\n-<<label=funcdef,include=FALSE,echo=FALSE>>=\n-genreporttable<-function(comparisons,ngenes,direction,fdr1,fdr5,fdr25){\n- xtb=data.frame(Comparison=comparisons,Genes=ngenes,Selection=direction,FDR1=fdr1,FDR5=fdr5,FDR25=fdr25);\n- colnames(xtb)=c("Comparison","Genes","Selection","FDR1%","FDR5%","FDR25%");\n- return (xtb);\n-}\n-@\n-\n-The statistics of comparisons is as indicated in the following table. \n-\n-<<label=tab1,echo=FALSE,results=tex>>=\n-library(xtable)\n-comparisons=c("HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.","HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.");\n-ngenes=c(100,100);\n-direction=c("negative","positive");\n-fdr1=c(0,0);\n-fdr5=c(2,0);\n-fdr25=c(9,1);\n-\n-cptable=genreporttable(comparisons,ngenes,direction,fdr1,fdr5,fdr25);\n-print(xtable(cptable, caption = "Summary of comparisons", label = "tab:one",\n- digits = c(0, 0, 0, 0, 0, 0, 0),\n- table.placement = "tbp",\n- caption.placement = "top"))\n-@\n-\n-The meanings of the columns are as follows.\n-\n-\\begin{itemize}\n-\\item \\textbf{Comparison}: The label for comparisons;\n-\\item \\textbf{Genes}: The number of genes in the library;\n-\\item \\textbf{Selection}: The direction of selection, either positive selection or negative selection;\n-\\item \\textbf{FDR1\\%}: The number of genes with FDR $<$ 1\\%;\n-\\item \\textbf{FDR5\\%}: The number of genes with FDR $<$ 5\\%;\n-\\item \\textbf{FDR25\\%}: The number of genes with FDR $<$ 25\\%;\n-\\end{itemize}\n-\n-The following figures show:\n-\n-\\begin{itemize}\n-\\item Individual sgRNA read counts of selected genes in selected samples; \n-\\item The distribution of RRA scores and p values of all genes; and\n-\\item The RRA scores and p values of selected genes.\n-\\end{itemize}\n-\n-\n-\\newpage\\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg.}\n-\n-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.\n-\n-<<echo=FALSE>>=\n-gstable=read.table(\'output.gene_summary.txt\',header=T)\n-@\n-%\n-\n-\n-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# \n-#\n-# parameters\n-# Do not modify the variables beginning with "__"\n-\n-# gstablename=\'__GENE_SUMMARY_FILE__\'\n-startindex=3\n-# outputfile=\'__OUTPUT_FILE__\'\n-targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1")\n-# samplelabel=sub(\'.\\w+.\\w+$\',\'\',colnames(gstable)[startindex]);\n-samplelabel=\'HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.\'\n-\n-\n-# You need to write some codes in front of this code:\n-# gstable=read.table(gstablename,header=T)\n-# pdf(file=outputfile,width=6,height=6)\n-\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-######\n-# function definition\n-\n-plotrankedvalues<-function(val, tglist, ...){\n- \n- plot(val,log=\'y\',ylim=c(max(val),min(val)),type=\'l\',lwd=2, ...)\n- if(length(tglist)>0){\n- for(i in 1:length(tglist)){\n- targetgene=tglist[i];\n- tx=which(names(val)==targetgene);ty=val[targetgene];\n- points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=2'..b'840312603593),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))\n-targetgene="ACSS2"\n-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-\n-## code\n-\n-targetmatvec=unlist(targetmat)+1\n-yrange=range(targetmatvec[targetmatvec>0]);\n-# yrange[1]=1; # set the minimum value to 1\n-for(i in 1:length(targetmat)){\n- vali=targetmat[[i]]+1;\n- if(i==1){\n- 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\')\n- axis(1,at=1:length(vali),labels=(collabel),las=2)\n- # lines(0:100,rep(1,101),col=\'black\');\n- }else{\n- lines(1:length(vali),vali,type=\'b\',pch=20,col=colors[(i %% length(colors))])\n- }\n-}\n-\n-\n-\n-# parameters\n-# Do not modify the variables beginning with "__"\n-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))\n-targetgene="ADNP"\n-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")\n-\n-# set up color using RColorBrewer\n-#library(RColorBrewer)\n-#colors <- brewer.pal(length(targetgenelist), "Set1")\n-\n-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF",\n- "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", \n- "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",\n- "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")\n-\n-\n-## code\n-\n-targetmatvec=unlist(targetmat)+1\n-yrange=range(targetmatvec[targetmatvec>0]);\n-# yrange[1]=1; # set the minimum value to 1\n-for(i in 1:length(targetmat)){\n- vali=targetmat[[i]]+1;\n- if(i==1){\n- 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\')\n- axis(1,at=1:length(vali),labels=(collabel),las=2)\n- # lines(0:100,rep(1,101),col=\'black\');\n- }else{\n- lines(1:length(vali),vali,type=\'b\',pch=20,col=colors[(i %% length(colors))])\n- }\n-}\n-\n-\n-\n-par(mfrow=c(1,1));\n-@\n-%__INDIVIDUAL_PAGE__\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\\end{document}\n-\n' |