Last updated: 2019-06-20
Checks: 6 0
Knit directory: apaQTL/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.3.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190411)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/.DS_Store
Ignored: output/.DS_Store
Untracked files:
Untracked: .Rprofile
Untracked: ._.DS_Store
Untracked: .gitignore
Untracked: _workflowr.yml
Untracked: analysis/._PASdescriptiveplots.Rmd
Untracked: analysis/._cuttoffPercUsage.Rmd
Untracked: analysis/QTLexampleplots.Rmd
Untracked: analysis/cuttoffPercUsage.Rmd
Untracked: analysis/eQTLoverlap.Rmd
Untracked: analysis/mergeRNA.Rmd
Untracked: analysis/oldstuffNotNeeded.Rmd
Untracked: apaQTL.Rproj
Untracked: code/.NascentRNAdtPlotFirstintronicPAS.sh.swp
Untracked: code/._ApaQTL_nominalNonnorm.sh
Untracked: code/._BothFracDTPlotGeneRegions_normalized.sh
Untracked: code/._EandPqtls.sh
Untracked: code/._FC_NucintornUpandDown.sh
Untracked: code/._FC_UTR.sh
Untracked: code/._FC_intornUpandDownsteamPAS.sh
Untracked: code/._FC_nascentseq.sh
Untracked: code/._FC_newPeaks_olddata.sh
Untracked: code/._HMMpermuteTotal.py
Untracked: code/._HmmPermute.py
Untracked: code/._LC_samplegroups.py
Untracked: code/._NascentRNAdtPlot.sh
Untracked: code/._NascentRNAdtPlot3UTRPAS.sh
Untracked: code/._NascentRNAdtPlotExcludeFirstintronicPAS.sh
Untracked: code/._NascentRNAdtPlotNucPAS.sh
Untracked: code/._NascentRNAdtPlotTotPAS.sh
Untracked: code/._NascentRNAdtPlotintronicPAS.sh
Untracked: code/._NascnetRNAdtPlotPAS.sh
Untracked: code/._NetSeq_fourthintronDT.sh
Untracked: code/._QTL2bed.py
Untracked: code/._QTL2bed_withstrand.py
Untracked: code/._RNAbam2bw.sh
Untracked: code/._SnakefilePAS
Untracked: code/._SnakefilefiltPAS
Untracked: code/._TESplots100bp.sh
Untracked: code/._TESplots150bp.sh
Untracked: code/._TESplots200bp.sh
Untracked: code/._Untitled
Untracked: code/._ZipandTabPheno.sh
Untracked: code/._aAPAqtl_nominal39ind.sh
Untracked: code/._apaQTLCorrectPvalMakeQQ.R
Untracked: code/._apaQTL_Nominal.sh
Untracked: code/._apaQTL_permuted.sh
Untracked: code/._assignNucIntonpeak2intronlocs.sh
Untracked: code/._assignTotIntronpeak2intronlocs.sh
Untracked: code/._bam2BW_5primemost.sh
Untracked: code/._bed2saf.py
Untracked: code/._bothFracDTplot1stintron.sh
Untracked: code/._bothFracDTplot4thintron.sh
Untracked: code/._bothFrac_FC.sh
Untracked: code/._callPeaksYL.py
Untracked: code/._changenomQTLres2geneName.py
Untracked: code/._chooseAnno2SAF.py
Untracked: code/._chooseSignalSite
Untracked: code/._chooseSignalSite.py
Untracked: code/._cluster.json
Untracked: code/._clusterPAS.json
Untracked: code/._clusterfiltPAS.json
Untracked: code/._codingdms2bed.py
Untracked: code/._config.yaml
Untracked: code/._config2.yaml
Untracked: code/._configOLD.yaml
Untracked: code/._convertNominal2SNPLOC.py
Untracked: code/._convertNumeric.py
Untracked: code/._correctNomeqtl.R
Untracked: code/._dag.pdf
Untracked: code/._eQTLgenestestedapa.py
Untracked: code/._encodeRNADTplots.sh
Untracked: code/._extractGenotypes.py
Untracked: code/._extractseqfromqtlfastq.py
Untracked: code/._fc2leafphen.py
Untracked: code/._filter5perc.R
Untracked: code/._filter5percPheno.py
Untracked: code/._filterpeaks.py
Untracked: code/._finalPASbed2SAF.py
Untracked: code/._fix4su304corr.py
Untracked: code/._fix4su604corr.py
Untracked: code/._fix4sukalisto.py
Untracked: code/._fixExandUnexeQTL
Untracked: code/._fixExandUnexeQTL.py
Untracked: code/._fixFChead.py
Untracked: code/._fixFChead_bothfrac.py
Untracked: code/._fixH3k12ac.py
Untracked: code/._fixRNAhead4corr.py
Untracked: code/._fixRNAkalisto.py
Untracked: code/._fixgroupedtranscript.py
Untracked: code/._fixhead_netseqfc.py
Untracked: code/._getAPAfromanyeQTL.py
Untracked: code/._getApapval4eqtl.py
Untracked: code/._getApapval4eqtl_unexp.py
Untracked: code/._getDownstreamIntronNuclear.py
Untracked: code/._getIntronDownstreamPAS.py
Untracked: code/._getIntronUpstreamPAS.py
Untracked: code/._getQTLalleles.py
Untracked: code/._getQTLfastq.sh
Untracked: code/._getUpstreamIntronNuclear.py
Untracked: code/._grouptranscripts.py
Untracked: code/._keep5perMAF.py
Untracked: code/._keepSNP_vcf.sh
Untracked: code/._make5percPeakbed.py
Untracked: code/._makeFileID.py
Untracked: code/._makePheno.py
Untracked: code/._makeSAFbothfrac5perc.py
Untracked: code/._makeSNP2rsidfile.py
Untracked: code/._makeeQTLempirical_unexp.py
Untracked: code/._makeeQTLempiricaldist.py
Untracked: code/._makegencondeTSSfile.py
Untracked: code/._mergRNABam.sh
Untracked: code/._mergeAllBam.sh
Untracked: code/._mergeBW_norm.sh
Untracked: code/._mergeBamNascent.sh
Untracked: code/._mergeByFracBam.sh
Untracked: code/._mergePeaks.sh
Untracked: code/._mnase1stintron.sh
Untracked: code/._mnaseDT_fourthintron.sh
Untracked: code/._namePeaks.py
Untracked: code/._netseqDTplot1stIntron.sh
Untracked: code/._netseqFC.sh
Untracked: code/._peak2PAS.py
Untracked: code/._peakFC.sh
Untracked: code/._pheno2countonly.R
Untracked: code/._phenoQTLfromlist.py
Untracked: code/._processYRIgen.py
Untracked: code/._qtlRegionseq.sh
Untracked: code/._qtlsPvalOppFrac.py
Untracked: code/._quantassign2parsedpeak.py
Untracked: code/._removeXfromHmm.py
Untracked: code/._removeloc_pheno.py
Untracked: code/._runCorrectNomEqtl.sh
Untracked: code/._runHMMpermuteAPAqtls.sh
Untracked: code/._runHMMpermuteeQTLS.sh
Untracked: code/._runMakeEmpiricaleQTL_unexp.sh
Untracked: code/._runMakeeQTLempirical.sh
Untracked: code/._run_getApaPval4eqtl.sh
Untracked: code/._run_getapafromeQTL.py
Untracked: code/._run_getapafromeQTL.sh
Untracked: code/._run_getapapval4eqtl_unexp.sh
Untracked: code/._run_leafcutterDiffIso.sh
Untracked: code/._run_sepUsagephen.sh
Untracked: code/._run_sepgenobychrom.sh
Untracked: code/._selectNominalPvalues.py
Untracked: code/._sepUsagePhen.py
Untracked: code/._sepgenobychrom.py
Untracked: code/._snakemakePAS.batch
Untracked: code/._snakemakefiltPAS.batch
Untracked: code/._sortindexRNAbam.sh
Untracked: code/._submit-snakemakePAS.sh
Untracked: code/._submit-snakemakefiltPAS.sh
Untracked: code/._subsetAPAnotEorPgene.py
Untracked: code/._subsetApanoteGene.py
Untracked: code/._subsetUnexplainedeQTLs.py
Untracked: code/._subset_diffisopheno.py
Untracked: code/._subsetpermAPAwithGenelist.py
Untracked: code/._subtrachfiveprimeUTR.sh
Untracked: code/._subtractExons.sh
Untracked: code/._subtractfiveprimeUTR.sh
Untracked: code/._tabixSNPS.sh
Untracked: code/._utrdms2saf.py
Untracked: code/.snakemake/
Untracked: code/APAqtl_nominal.err
Untracked: code/APAqtl_nominal.out
Untracked: code/APAqtl_nominal_39.err
Untracked: code/APAqtl_nominal_39.out
Untracked: code/APAqtl_nominal_nonNorm.err
Untracked: code/APAqtl_nominal_nonNorm.out
Untracked: code/APAqtl_permuted.err
Untracked: code/APAqtl_permuted.out
Untracked: code/ApaQTL_nominalNonnorm.sh
Untracked: code/BothFracDTPlot1stintron.err
Untracked: code/BothFracDTPlot1stintron.out
Untracked: code/BothFracDTPlot4stintron.err
Untracked: code/BothFracDTPlot4stintron.out
Untracked: code/BothFracDTPlotGeneRegions.err
Untracked: code/BothFracDTPlotGeneRegions.out
Untracked: code/BothFracDTPlotGeneRegions_norm.err
Untracked: code/BothFracDTPlotGeneRegions_norm.out
Untracked: code/BothFracDTPlotGeneRegions_normalized.sh
Untracked: code/DistPAS2Sig.py
Untracked: code/EandPqtl.err
Untracked: code/EandPqtl.out
Untracked: code/EandPqtls.sh
Untracked: code/EncodeRNADTPlotGeneRegions.err
Untracked: code/EncodeRNADTPlotGeneRegions.out
Untracked: code/FC_NucintornUpandDown.sh
Untracked: code/FC_NucintronPASupandDown.err
Untracked: code/FC_NucintronPASupandDown.out
Untracked: code/FC_UTR.err
Untracked: code/FC_UTR.out
Untracked: code/FC_UTR.sh
Untracked: code/FC_intornUpandDownsteamPAS.sh
Untracked: code/FC_intronPASupandDown.err
Untracked: code/FC_intronPASupandDown.out
Untracked: code/FC_nascent.err
Untracked: code/FC_nascentout
Untracked: code/FC_nascentseq.sh
Untracked: code/FC_newPAS_olddata.err
Untracked: code/FC_newPAS_olddata.out
Untracked: code/FC_newPeaks_olddata.sh
Untracked: code/HMMpermuteTotal.py
Untracked: code/HmmPermute.p
Untracked: code/HmmPermute.py
Untracked: code/LC_samplegroups.py
Untracked: code/NascentDTPlotGeneRegions.err
Untracked: code/NascentDTPlotGeneRegions.out
Untracked: code/NascentDTPlotPAS.err
Untracked: code/NascentDTPlotPAS.out
Untracked: code/NascentDTPlotPAS_3utr.err
Untracked: code/NascentDTPlotPAS_3utr.out
Untracked: code/NascentDTPlotPAS_firstintron.err
Untracked: code/NascentDTPlotPAS_firstintron.out
Untracked: code/NascentDTPlotPAS_intron.err
Untracked: code/NascentDTPlotPAS_intron.out
Untracked: code/NascentDTPlotPAS_nuc.err
Untracked: code/NascentDTPlotPAS_nuc.out
Untracked: code/NascentDTPlotPAS_tot.err
Untracked: code/NascentDTPlotPAS_tot.out
Untracked: code/NascentRNAdtPlot.sh
Untracked: code/NascentRNAdtPlot3UTRPAS.sh
Untracked: code/NascentRNAdtPlotExcludeFirstintronicPAS.sh
Untracked: code/NascentRNAdtPlotFirstintronicPAS.sh
Untracked: code/NascentRNAdtPlotNucPAS.sh
Untracked: code/NascentRNAdtPlotTotPAS.sh
Untracked: code/NascentRNAdtPlotintronicPAS.sh
Untracked: code/NascnetRNAdtPlotPAS.sh
Untracked: code/NetSeq_fourthintronDT.sh
Untracked: code/Nuclear_example.err
Untracked: code/Nuclear_example.out
Untracked: code/QTL2bed.py
Untracked: code/QTL2bed_withstrand.py
Untracked: code/README.md
Untracked: code/RNABam2BW.err
Untracked: code/RNABam2BW.out
Untracked: code/RNAbam2bw.sh
Untracked: code/Rplots.pdf
Untracked: code/Script4NuclearQTLexamples.sh
Untracked: code/Script4TotalQTLexamples.sh
Untracked: code/TESplots100bp.err
Untracked: code/TESplots100bp.out
Untracked: code/TESplots100bp.sh
Untracked: code/TESplots150bp.err
Untracked: code/TESplots150bp.out
Untracked: code/TESplots150bp.sh
Untracked: code/TESplots200bp.err
Untracked: code/TESplots200bp.out
Untracked: code/TESplots200bp.sh
Untracked: code/Total_example.err
Untracked: code/Total_example.out
Untracked: code/Untitled
Untracked: code/Upstream100Bases_general.py
Untracked: code/ZipandTabPheno.sh
Untracked: code/aAPAqtl_nominal39ind.sh
Untracked: code/apaQTLCorrectPvalMakeQQ_4pc.R
Untracked: code/apaQTL_Nominal_4pc.sh
Untracked: code/apaQTL_permuted.4pc.sh
Untracked: code/apafacetboxplots.R
Untracked: code/apaqtlfacetboxplots.R
Untracked: code/assignNucIntonpeak2intronlocs.sh
Untracked: code/assignPeak2Intronicregion.err
Untracked: code/assignPeak2Intronicregion.out
Untracked: code/assignTotIntronpeak2intronlocs.sh
Untracked: code/assigntotPeak2Intronicregion.err
Untracked: code/assigntotPeak2Intronicregion.out
Untracked: code/bam2BW_5primemost.sh
Untracked: code/bam2bw.err
Untracked: code/bam2bw.out
Untracked: code/bam2bw_5primemost.err
Untracked: code/bam2bw_5primemost.out
Untracked: code/bothFracDTplot1stintron.sh
Untracked: code/bothFracDTplot4thintron.sh
Untracked: code/bothFrac_FC.err
Untracked: code/bothFrac_FC.out
Untracked: code/bothFrac_FC.sh
Untracked: code/changenomQTLres2geneName.py
Untracked: code/codingdms2bed.py
Untracked: code/convertNominal2SNPLOC.py
Untracked: code/correctNomeqtl.R
Untracked: code/dag.pdf
Untracked: code/dagPAS.pdf
Untracked: code/dagfiltPAS.pdf
Untracked: code/eQTLgenestestedapa.py
Untracked: code/encodeRNADTplots.sh
Untracked: code/extractGenotypes.py
Untracked: code/extractseqfromqtlfastq.py
Untracked: code/fc2leafphen.py
Untracked: code/finalPASbed2SAF.py
Untracked: code/findbuginpeaks.R
Untracked: code/fix4su304corr.py
Untracked: code/fix4su604corr.py
Untracked: code/fix4sukalisto.py
Untracked: code/fixExandUnexeQTL
Untracked: code/fixExandUnexeQTL.py
Untracked: code/fixFChead_bothfrac.py
Untracked: code/fixFChead_summary.py
Untracked: code/fixH3k12ac.py
Untracked: code/fixRNAhead4corr.py
Untracked: code/fixRNAkalisto.py
Untracked: code/fixgroupedtranscript.py
Untracked: code/fixhead_netseqfc.py
Untracked: code/genotypesYRI.gen.proc.keep.vcf.log
Untracked: code/genotypesYRI.gen.proc.keep.vcf.recode.vcf
Untracked: code/get100upPAS.py
Untracked: code/getAPAfromanyeQTL.py
Untracked: code/getApapval4eqtl.py
Untracked: code/getApapval4eqtl_unexp.py
Untracked: code/getDownstreamIntronNuclear.py
Untracked: code/getIntronDownstreamPAS.py
Untracked: code/getIntronUpstreamPAS.py
Untracked: code/getQTLalleles.py
Untracked: code/getQTLfastq.sh
Untracked: code/getSeq100up.sh
Untracked: code/getUpstreamIntronNuclear.py
Untracked: code/getseq100up.err
Untracked: code/getseq100up.out
Untracked: code/grouptranscripts.err
Untracked: code/grouptranscripts.out
Untracked: code/grouptranscripts.py
Untracked: code/keep5perMAF.py
Untracked: code/keepSNP_vcf.sh
Untracked: code/log/
Untracked: code/makeSAFbothfrac5perc.py
Untracked: code/makeSNP2rsidfile.py
Untracked: code/makeeQTLempirical_unexp.py
Untracked: code/makeeQTLempiricaldist.py
Untracked: code/makegencondeTSSfile.py
Untracked: code/mergRNABam.sh
Untracked: code/mergeBW_norm.sh
Untracked: code/mergeBWnorm.err
Untracked: code/mergeBWnorm.out
Untracked: code/mergeBamNacent.err
Untracked: code/mergeBamNacent.out
Untracked: code/mergeBamNascent.sh
Untracked: code/mergeRNAbam.err
Untracked: code/mergeRNAbam.out
Untracked: code/mnase1stintron.sh
Untracked: code/mnaseDTPlot1stintron.err
Untracked: code/mnaseDTPlot1stintron.out
Untracked: code/mnaseDTPlot4thintron.err
Untracked: code/mnaseDTPlot4thintron.out
Untracked: code/mnaseDT_fourthintron.sh
Untracked: code/netDTPlot4thintron.out
Untracked: code/netseqDTplot1stIntron.sh
Untracked: code/netseqFC.err
Untracked: code/netseqFC.out
Untracked: code/netseqFC.sh
Untracked: code/neyDTPlot4thintron.err
Untracked: code/phenoQTLfromlist.py
Untracked: code/processYRIgen.py
Untracked: code/qtlFacetBoxplots.err
Untracked: code/qtlFacetBoxplots.out
Untracked: code/qtlRegionseq.sh
Untracked: code/qtlsPvalOppFrac.py
Untracked: code/removeXfromHmm.py
Untracked: code/removeloc_pheno.py
Untracked: code/runCorrectNomEqtl.sh
Untracked: code/runCorrectNomeqtl.err
Untracked: code/runCorrectNomeqtl.out
Untracked: code/runHMMpermute.err
Untracked: code/runHMMpermute.out
Untracked: code/runHMMpermuteAPAqtls.sh
Untracked: code/runHMMpermuteeQTLS.sh
Untracked: code/runHMMpermuteeQTLs.err
Untracked: code/runHMMpermuteeQTLs.out
Untracked: code/runMakeEmpiricaleQTL_unexp.sh
Untracked: code/runMakeEmpiricaleQTLs.err
Untracked: code/runMakeEmpiricaleQTLs.out
Untracked: code/runMakeEmpiricaleQTLsunex.err
Untracked: code/runMakeEmpiricaleQTLsunex.out
Untracked: code/runMakeeQTLempirical.sh
Untracked: code/run_DistPAS2Sig.err
Untracked: code/run_DistPAS2Sig.out
Untracked: code/run_distPAS2Sig.sh
Untracked: code/run_getAPAfromanyeQTL.err
Untracked: code/run_getAPAfromanyeQTL.out
Untracked: code/run_getApaPval4eQTLs.err
Untracked: code/run_getApaPval4eQTLs.out
Untracked: code/run_getApaPval4eQTLsunexplained.err
Untracked: code/run_getApaPval4eQTLsunexplained.out
Untracked: code/run_getApaPval4eqtl.sh
Untracked: code/run_getapafromeQTL.sh
Untracked: code/run_getapapval4eqtl_unexp.sh
Untracked: code/run_leafcutterDiffIso.sh
Untracked: code/run_leafcutter_ds.err
Untracked: code/run_leafcutter_ds.out
Untracked: code/run_qtlFacetBoxplots.sh
Untracked: code/run_sepUsagephen.sh
Untracked: code/run_sepgenobychrom.err
Untracked: code/run_sepgenobychrom.out
Untracked: code/run_sepgenobychrom.sh
Untracked: code/run_sepusage.err
Untracked: code/run_sepusage.out
Untracked: code/selectNominalPvalues.py
Untracked: code/sepUsagePhen.py
Untracked: code/sepgenobychrom.py
Untracked: code/seqQTLfastq.err
Untracked: code/seqQTLfastq.out
Untracked: code/seqQTLregion.err
Untracked: code/seqQTLregion.out
Untracked: code/snakePASlog.out
Untracked: code/snakefiltPASlog.out
Untracked: code/sortindexRNABam.err
Untracked: code/sortindexRNABam.out
Untracked: code/sortindexRNAbam.sh
Untracked: code/subsetAPAnotEorPgene.py
Untracked: code/subsetApanoteGene.py
Untracked: code/subsetUnexplainedeQTLs.py
Untracked: code/subset_diffisopheno.py
Untracked: code/subsetpermAPAwithGenelist.py
Untracked: code/subtract5UTR.err
Untracked: code/subtract5UTR.out
Untracked: code/subtractExons.err
Untracked: code/subtractExons.out
Untracked: code/subtractExons.sh
Untracked: code/subtractfiveprimeUTR.sh
Untracked: code/tabixSNPS.sh
Untracked: code/tabixSNPs.err
Untracked: code/tabixSNPs.out
Untracked: code/transcriptdm2bed.py
Untracked: code/utrdms2saf.py
Untracked: code/vcf_keepsnps.err
Untracked: code/vcf_keepsnps.out
Untracked: code/writeExampleQTLcode.py
Untracked: code/zipandtabPhen.err
Untracked: code/zipandtabPhen.out
Untracked: data/._.DS_Store
Untracked: data/ApaByEgene/
Untracked: data/ApaByPgene/
Untracked: data/Battle_pQTL/
Untracked: data/CompareOldandNew/
Untracked: data/DTmatrix/
Untracked: data/DiffIso/
Untracked: data/EncodeRNA/
Untracked: data/ExampleQTLPlots/
Untracked: data/GeuvadisRNA/
Untracked: data/HMMqtls/
Untracked: data/Li_eQTLs/
Untracked: data/NascentRNA/
Untracked: data/NucSpeceQTLeffect/
Untracked: data/PAS/
Untracked: data/QTLGenotypes/
Untracked: data/QTLoverlap/
Untracked: data/QTLoverlap_nonNorm/
Untracked: data/README.md
Untracked: data/RNAseq/
Untracked: data/Reads2UTR/
Untracked: data/SignalSiteFiles/
Untracked: data/ThirtyNineIndQtl_nominal/
Untracked: data/apaQTLNominal/
Untracked: data/apaQTLNominal_4pc/
Untracked: data/apaQTLPermuted/
Untracked: data/apaQTLPermuted_4pc/
Untracked: data/apaQTLs/
Untracked: data/assignedPeaks/
Untracked: data/bam/
Untracked: data/bam_clean/
Untracked: data/bam_waspfilt/
Untracked: data/bed_10up/
Untracked: data/bed_clean/
Untracked: data/bed_clean_sort/
Untracked: data/bed_waspfilter/
Untracked: data/bedsort_waspfilter/
Untracked: data/bothFrac_FC/
Untracked: data/bw_norm/
Untracked: data/eQTLs/
Untracked: data/exampleQTLs/
Untracked: data/fastq/
Untracked: data/filterPeaks/
Untracked: data/fourSU/
Untracked: data/h3k27ac/
Untracked: data/highdiffsiggenes.txt
Untracked: data/inclusivePeaks/
Untracked: data/inclusivePeaks_FC/
Untracked: data/intronRNAratio/
Untracked: data/intron_analysis/
Untracked: data/mergedBG/
Untracked: data/mergedBW_byfrac/
Untracked: data/mergedBW_norm/
Untracked: data/mergedBam/
Untracked: data/mergedbyFracBam/
Untracked: data/molPhenos/
Untracked: data/molQTLs/
Untracked: data/motifdistrupt/
Untracked: data/netseq/
Untracked: data/nonNorm_pheno/
Untracked: data/nuc_10up/
Untracked: data/nuc_10upclean/
Untracked: data/overlapeQTL_try2/
Untracked: data/overlapeQTLs/
Untracked: data/peakCoverage/
Untracked: data/peaks_5perc/
Untracked: data/phenotype/
Untracked: data/phenotype_5perc/
Untracked: data/sigDiffGenes.txt
Untracked: data/sort/
Untracked: data/sort_clean/
Untracked: data/sort_waspfilter/
Untracked: nohup.out
Untracked: output/._.DS_Store
Untracked: output/._meanCorrelationPhenotypes.svg
Untracked: output/dtPlots/
Untracked: output/fastqc/
Untracked: output/meanCorrelationPhenotypes.svg
Unstaged changes:
Modified: analysis/NuclearSpecAPAqtl.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/pQTLandeQTLoverlap.Rmd
Modified: analysis/signalsiteanalysis.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
Deleted: code/Upstream10Bases_general.py
Modified: code/apaQTLCorrectPvalMakeQQ.R
Modified: code/apaQTL_Nominal.sh
Modified: code/apaQTL_permuted.sh
Modified: code/apaQTLsnake.err
Modified: code/bam2bw.sh
Modified: code/bed2saf.py
Modified: code/cluster.json
Modified: code/clusterfiltPAS.json
Modified: code/config.yaml
Modified: code/environment.yaml
Modified: code/makePheno.py
Deleted: code/test.txt
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | b7c9381 | brimittleman | 2019-06-20 | test inc/dec |
html | cd60f50 | brimittleman | 2019-06-20 | Build site. |
Rmd | 6df08b6 | brimittleman | 2019-06-20 | change analysis to include not tested in total as nuc spec |
In my previous analysis found here I took nuclear specific apa QTLs as those tested in total that are not nominally significant in total. In this analysis I will include the nuclear apaQTLs in PAS not tested in total as nuclear specific. These may be important for explaining eQTLs or pQTLs.
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
library(ggpubr)
Loading required package: magrittr
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'ggpubr'
The following object is masked from 'package:cowplot':
get_legend
I will give all of the QTLs an id.
nucQTls=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% mutate(ID=paste(Gene,Peak, sid, sep=":"))
sharedQTLs=read.table("../data/apaQTLs/SharedAPAQTLs.txt", header = T, stringsAsFactors = F) %>% mutate(ID=paste(gene,peakNum, snp, sep=":"))
sharedQTL_ID=as.vector(sharedQTLs$ID)
Nuclear Specific:
NuclearSpecQTL= nucQTls %>% mutate(Shared=ifelse(ID %in% sharedQTL_ID, "Yes", "No"))
NuclearSpecQTL$Shared=as.factor(NuclearSpecQTL$Shared)
I need to input the explained eGenes, unexplained eGenes, and pGenes. For this I will make sure none of the pgenes are eGenes.
explained=read.table("../data/Li_eQTLs/explainedEgenes.txt", header = F, stringsAsFactors = F, col.names = c("gene"))
unexplained=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt", header = F, stringsAsFactors = F, col.names = c("gene"))
protein=read.table("../data/Battle_pQTL/psQTLGeneNames.txt",header = F, stringsAsFactors = F,col.names = c("gene"))
'%!in%' <- function(x,y)!('%in%'(x,y))
protein_only=protein %>% filter(gene %!in% explained$gene & gene %!in% unexplained$gene)
write.table(protein_only, "../data/Battle_pQTL/pQTLGeneNamesONLYP.txt", col.names = F, row.names = F,quote = F, sep="\t")
Are nuc specific less likely to be in p genes?
NuclearSpecQTL_gene=NuclearSpecQTL %>% mutate(pGene=ifelse(Gene %in% protein_only$gene, "Yes", "No"), uneplained=ifelse(Gene %in% unexplained$gene, "Yes", "No"), explained=ifelse(Gene %in% explained$gene, "Yes","No"))
nPandShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="Yes", pGene=="Yes"))/nrow(NuclearSpecQTL_gene)
nPandShare
[1] 0.01037613
nPandNotShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="No", pGene=="Yes"))/nrow(NuclearSpecQTL_gene)
nPandNotShare
[1] 0.002594034
Only looking at 8 and 2. This isnt very good. Cant make claim.
nEandShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="Yes", uneplained=="Yes" |explained=="Yes" ))
nEandShare
[1] 113
nEandNotShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="No", uneplained=="Yes" |explained=="Yes"))
nEandNotShare
[1] 59
prop.test(x=c(nEandShare,nEandNotShare),n=c(nrow(NuclearSpecQTL_gene),nrow(NuclearSpecQTL_gene)))
2-sample test for equality of proportions with continuity
correction
data: c(nEandShare, nEandNotShare) out of c(nrow(NuclearSpecQTL_gene), nrow(NuclearSpecQTL_gene))
X-squared = 18.382, df = 1, p-value = 1.808e-05
alternative hypothesis: two.sided
95 percent confidence interval:
0.03751188 0.10256594
sample estimates:
prop 1 prop 2
0.14656291 0.07652399
I want to not count genes with multiple qtl
nGenes=NuclearSpecQTL_gene %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
nGenes
[1] 609
Egeneandshared=NuclearSpecQTL_gene %>% filter(Shared=="Yes", uneplained=="Yes" |explained=="Yes" ) %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
Egeneandshared
[1] 89
EgeneandNotshared=NuclearSpecQTL_gene %>% filter(Shared=="No", uneplained=="Yes" |explained=="Yes" ) %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
EgeneandNotshared
[1] 53
prop.test(x=c(Egeneandshared,EgeneandNotshared),n=c(nGenes,nGenes))
2-sample test for equality of proportions with continuity
correction
data: c(Egeneandshared, EgeneandNotshared) out of c(nGenes, nGenes)
X-squared = 9.7652, df = 1, p-value = 0.001778
alternative hypothesis: two.sided
95 percent confidence interval:
0.02157843 0.09664817
sample estimates:
prop 1 prop 2
0.14614122 0.08702791
This is significant. This means the extra PAS are most likely driving the egene overlap.
Write these out for other anaylsis.
NuclearSpecQTL_shared= NuclearSpecQTL %>% filter(Shared=="Yes") %>% select(Gene, sid)
write.table(NuclearSpecQTL_shared,file="../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt", col.names = F, row.names = F, sep="\t", quote = F )
NuclearSpecQTL_specific=NuclearSpecQTL %>% filter(Shared=="No")%>% select(Gene, sid)
write.table(NuclearSpecQTL_specific,file="../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt", col.names = F, row.names = F, sep="\t", quote = F )
ggplot(NuclearSpecQTL,aes(x=Loc, fill=Shared)) + geom_bar()
NuclearSpecQTL__group= NuclearSpecQTL %>% group_by(Loc, Shared) %>% summarise(nShared=n()) %>% ungroup() %>% group_by(Loc) %>% mutate(nLoc=sum(nShared)) %>% ungroup() %>% mutate(prop=nShared/nLoc)
ggplot(NuclearSpecQTL__group, aes(x=Loc, y=prop, fill=Shared)) + geom_bar(stat="identity") + labs(title="Proportion of apaQTL by \nlocation that are nuclear specific")
NuclearSpecQTL__group_small=NuclearSpecQTL__group %>% filter( Loc=="intron" |Loc=="utr3")
ggplot(NuclearSpecQTL__group_small, aes(x=Loc, y=prop, fill=Shared)) + geom_bar(stat="identity") + labs(title="Proportion of apaQTL by \nlocation that are nuclear specific")
NuclearSpecQTL__group_small
# A tibble: 4 x 5
Loc Shared nShared nLoc prop
<chr> <fct> <int> <int> <dbl>
1 intron No 183 297 0.616
2 intron Yes 114 297 0.384
3 utr3 No 87 355 0.245
4 utr3 Yes 268 355 0.755
prop.test(x=c(183,87),n=c(297,355))
2-sample test for equality of proportions with continuity
correction
data: c(183, 87) out of c(297, 355)
X-squared = 90.261, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
0.2968583 0.4453241
sample estimates:
prop 1 prop 2
0.6161616 0.2450704
I want to know if the shared or specific are more likely to decrease/increase
NuclearSpecQTL=NuclearSpecQTL %>% mutate(Dir=ifelse(slope>1, "Increase", "Decrease"))
NuclearSpecQTL_shareInc=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Increase", Shared=="Yes") %>% nrow()
AllShared=NuclearSpecQTL %>% filter(Loc=="intron", Shared=="Yes") %>% nrow()
AllInc=NuclearSpecQTL %>% filter(Loc=="intron", Dir=="Increase") %>% nrow()
AllDec=NuclearSpecQTL %>% filter(Loc=="intron", Dir=="Decrease") %>% nrow()
AllSpec=NuclearSpecQTL %>% filter(Loc=="intron", Shared=="No") %>% nrow()
NuclearSpecQTL_SpecInc=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Increase", Shared=="No") %>% nrow()
NuclearSpecQTL_shareDec=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Decrease", Shared=="Yes") %>% nrow()
NuclearSpecQTL_SpecDec=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Decrease", Shared=="No") %>% nrow()
#in increased
NuclearSpecQTL_SpecInc/AllInc
[1] 0.5701754
#in dec
NuclearSpecQTL_SpecDec/AllDec
[1] 0.6448087
prop.test(x=c(NuclearSpecQTL_SpecInc,NuclearSpecQTL_SpecDec), n=c(AllInc,AllDec))
2-sample test for equality of proportions with continuity
correction
data: c(NuclearSpecQTL_SpecInc, NuclearSpecQTL_SpecDec) out of c(AllInc, AllDec)
X-squared = 1.3538, df = 1, p-value = 0.2446
alternative hypothesis: two.sided
95 percent confidence interval:
-0.19605819 0.04679158
sample estimates:
prop 1 prop 2
0.5701754 0.6448087
ggplot(NuclearSpecQTL, aes(x=Dir, fill=Shared))+ geom_bar(stat="count") + facet_grid(~Loc) + theme(axis.text.x=element_text(angle=90, hjust=1))
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.2 magrittr_1.5 cowplot_0.9.4 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
[13] workflowr_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 reshape2_1.4.3 haven_1.1.2 lattice_0.20-38
[5] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6 yaml_2.2.0
[9] utf8_1.1.4 rlang_0.3.1 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 labeling_0.3 knitr_1.20 fansi_0.4.0
[25] broom_0.5.1 Rcpp_1.0.0 scales_1.0.0 backports_1.1.2
[29] jsonlite_1.6 fs_1.2.6 hms_0.4.2 digest_0.6.18
[33] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2 cli_1.0.1
[37] tools_3.5.1 lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[45] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[49] nlme_3.1-137 git2r_0.25.2 compiler_3.5.1