| Previous changeset 3:5dfc0e462f2a (2018-04-19) Next changeset 5:b36c85e37a48 (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 mageck_mle.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 5dfc0e462f2a -r b34c9d6373e0 mageck_macros.xml --- a/mageck_macros.xml Thu Apr 19 05:34:53 2018 -0400 +++ b/mageck_macros.xml Mon Jun 04 10:58:04 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> |
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| diff -r 5dfc0e462f2a -r b34c9d6373e0 mageck_mle.xml --- a/mageck_mle.xml Thu Apr 19 05:34:53 2018 -0400 +++ b/mageck_mle.xml Mon Jun 04 10:58:04 2018 -0400 |
| [ |
| @@ -46,10 +46,12 @@ --adjust-method $adv.adjust_method #end if -#if $adv.sgrnaeff_file: - --sgrna-efficiency $adv.sgrnaeff_file - --sgrna-eff-name-column $adv.sgrnaid_col - --sgrna-eff-score-column $adv.sgrnaeff_col +#if $adv.sgrnaeff.sgrnaeff_file_select == "yes": + #set $nindex = int(str($adv.sgrnaeff.sgrnaeff_name_col)) - 1 + #set $sindex = int(str($adv.sgrnaeff.sgrnaeff_score_col)) - 1 + --sgrna-efficiency $adv.sgrnaeff.sgrnaeff_file + --sgrna-eff-name-column $nindex + --sgrna-eff-score-column $sindex #end if $adv.remove_outliers @@ -98,9 +100,18 @@ <option value="holm">Holm</option> <option value="pounds">Pounds</option> </param> - <param name="sgrnaeff_file" argument="--sgrna-efficiency" type="data" format="tabular" optional="true" label="sgRNA efficiency file" help="An optional file of sgRNA efficiency prediction. The efficiency prediction will be used as an initial guess of the probability an sgRNA is efficient. Must contain at least two columns, one containing sgRNA ID, the other containing sgRNA efficiency prediction" /> - <param name="sgrnaeff_name_col" argument="--sgrna-eff-score-column" type="data_column" data_ref="sgrnaeff_file" value="1" optional="true" label="sgRNA score column" help="The sgRNA efficiency prediction column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 1 (the second column)." /> - <param name="sgrnaeff_score_col" argument="--sgrna-eff-name-column" type="data_column" data_ref="sgrnaeff_file" value="0" optional="true" label="sgRNA ID column" help="The sgRNA ID column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 0 (the first column)" /> + <conditional name="sgrnaeff"> + <param name="sgrnaeff_file_select" type="select" label="Incorporate sgRNA efficiency" help="Optionally sgRNA efficiency information can be incorporated into the analysis. See the MAGeCK website here for more information: https://sourceforge.net/p/mageck/wiki/Home/#tutorial-3-include-the-sgrna-efficiency-into-mle-calculation"> + <option value="yes">Yes</option> + <option value="no" selected="True">No</option> + </param> + <when value="yes"> + <param name="sgrnaeff_file" argument="--sgrna-efficiency" type="data" format="tabular" label="sgRNA efficiency file" help="A file of sgRNA efficiency prediction from the SSC program. The efficiency prediction will be used as an initial guess of the probability an sgRNA is efficient. Must contain at least two columns, one containing sgRNA ID, the other containing sgRNA efficiency prediction." /> + <param name="sgrnaeff_name_col" argument="--sgrna-eff-name-column" type="data_column" data_ref="sgrnaeff_file" value="1" label="sgRNA ID column" help="The sgRNA ID column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 1 (the first column)" /> + <param name="sgrnaeff_score_col" argument="--sgrna-eff-score-column" type="data_column" data_ref="sgrnaeff_file" value="2" label="sgRNA score column" help="The sgRNA efficiency prediction column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 2 (the second column)." /> + </when> + <when value="no"/> + </conditional> <param name="update_eff" argument="--update-efficiency" type="boolean" truevalue="--update-efficiency" falsevalue="" checked="false" optional="true" label="Update efficiency" help="Iteratively update sgRNA efficiency during EM iteration" /> <param name="out_log" type="boolean" truevalue="True" falsevalue="" checked="false" @@ -119,10 +130,40 @@ <param name="count_table" value="demo/demo1/sample.txt" ftype="tabular" /> <param name="design_matrix" ftype="tabular" value="in.mle.design_matrix.txt" /> <param name="out_log" value="True"/> - <output name="gene_summary" file="out.mle.gene_summary.txt" compare="sim_size"/> - <output name="sgrna_summary" file="out.mle.sgrna_summary.txt"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*beta.*z.*p-value.*fdr.*wald-p-value.*wald-fdr.*beta.*p-value.*fdr.*wald-p-value.*wald-fdr" /> + <has_text_matching expression="A1CF.*10.*0.05018.*0.3479.*0.7278.*0.8665.*0.0927.*0.6435.*0.5198.*0.8170"/> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*eff" /> + <has_text_matching expression="ADNP2.*ADNP2_m77891006.*1"/> + </assert_contents> + </output> <output name="log" file="out.mle.log.txt" compare="sim_size"/> </test> + <test><!-- Ensure sgRNA efficiency file works --> + <param name="count_table" value="demo/demo1/sample.txt" ftype="tabular" /> + <param name="design_matrix" ftype="tabular" value="in.mle.design_matrix.txt" /> + <param name="sgrnaeff_file_select" value="yes"/> + <param name="sgrnaeff_file" value="in.mle.sgrnaeff"/> + <param name="sgrnaeff_name_col" value="2"/> + <param name="sgrnaeff_score_col" value="4"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*beta.*z.*p-value.*fdr.*wald-p-value.*wald-fdr.*beta.*p-value.*fdr.*wald-p-value.*wald-fdr" /> + <has_text_matching expression="A1CF.*10.*0.05018.*0.3479.*0.7278.*0.8665.*0.0927.*0.6435.*0.5198.*0.8252"/> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*eff" /> + <has_text_matching expression="ADNP2.*ADNP2_m77891006.*0.646"/> + </assert_contents> + </output> + </test> </tests> <help><![CDATA[ |
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| diff -r 5dfc0e462f2a -r b34c9d6373e0 test-data/in.mle.sgrnaeff --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in.mle.sgrnaeff Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.count.bam.txt --- a/test-data/out.count.bam.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.bam.txt Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.count.fastq.txt --- a/test-data/out.count.fastq.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.fastq.txt Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.count.txt --- a/test-data/out.count.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.txt Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.count_multi.txt --- a/test-data/out.count_multi.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count_multi.txt Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.test.R --- a/test-data/out.test.R Thu Apr 19 05:34:53 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' |
| b |
| diff -r 5dfc0e462f2a -r b34c9d6373e0 test-data/out.test.log.txt --- a/test-data/out.test.log.txt Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| 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 5dfc0e462f2a -r b34c9d6373e0 test-data/out.test.report.pdf |
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| Binary file test-data/out.test.report.pdf has changed |
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| diff -r 5dfc0e462f2a -r b34c9d6373e0 test-data/out.test.sgrna_summary.txt --- a/test-data/out.test.sgrna_summary.txt Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| 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 5dfc0e462f2a -r b34c9d6373e0 test-data/output.count_normalized.txt --- a/test-data/output.count_normalized.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/output.count_normalized.txt Mon Jun 04 10:58:04 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 5dfc0e462f2a -r b34c9d6373e0 test-data/output_summary.Rnw --- a/test-data/output_summary.Rnw Thu Apr 19 05:34:53 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' |