Proportion eQTLs explained

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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20190411)

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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: eb847c1

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/Readdistagainstfeatures.Rmd
    Modified:   analysis/molQTL.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.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	eb847c1	brimittleman	2019-06-20	add analysis by pval
html	ca379ce	brimittleman	2019-06-13	Build site.
Rmd	2fd2b27	brimittleman	2019-06-13	fix bug
html	b907ac1	brimittleman	2019-06-12	Build site.
Rmd	178c5dc	brimittleman	2019-06-12	new geno
html	6b164c8	brimittleman	2019-06-07	Build site.
Rmd	b39620d	brimittleman	2019-06-07	add bonfor results
html	458e494	brimittleman	2019-06-07	Build site.
Rmd	32091ee	brimittleman	2019-06-07	more prop explained to new analysis

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(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

I need to fix the explained_FDR10.sort.txt and unexplained_FDR10.sort.txt files because right now this file has multiple genes per snp.

python fixExandUnexeQTL.py ../data/Li_eQTLs/explained_FDR10.sort.txt ../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt
python fixExandUnexeQTL.py ../data/Li_eQTLs/unexplained_FDR10.sort.txt ../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt

There are 1195 explained and 814 unexplained eQTLs. I will next look at each of these in my apadata.

Convert nominal results to have snps rather than rsids:

python convertNominal2SNPLOC.py Total
python convertNominal2SNPLOC.py Nuclear

mkdir ../data/overlapeQTL_try2
sbatch run_getapafromeQTL.sh

total

I can group the unexplained by gene and snp then I can ask if there is at least 1 significat peak for each of these.

I will use the bonforoni correction here and multiply the pvalue by the number of peaks in the gene:snp association.

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames)
totalapaUnexplained=totalapaUnexplained %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>% mutate(nPeaks=n(), adjPval=pval* nPeaks)%>%  dplyr::slice(which.min(adjPval))

totalapaUnexplained_sig= totalapaUnexplained %>% filter(adjPval<.05)

Look at distribution of these pvals:

ggplot(totalapaUnexplained, aes(x=adjPval)) + geom_histogram(bins=50)

Version	Author	Date
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

Proportion explained:

nrow(totalapaUnexplained_sig)/nrow(totalapaUnexplained)

[1] 0.1632653

I tested 588 unexplained eQTLs in the total fraction and 96 have a bonforoni corrected significant peak.

Compare to explained eQTLS:

totalapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

totalapaexplained_sig= totalapaexplained %>% filter(adjPval<.05)

nrow(totalapaexplained_sig)/nrow(totalapaexplained)

[1] 0.1304878

I am testing 820 explained eQTLs and of those 107 have a bonforoni corrected significant peak.

difference of proportions:

prop.test(x=c(nrow(totalapaUnexplained_sig),nrow(totalapaexplained_sig)), n=c(nrow(totalapaUnexplained),nrow(totalapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(totalapaUnexplained_sig), nrow(totalapaexplained_sig)) out of c(nrow(totalapaUnexplained), nrow(totalapaexplained))
X-squared = 2.722, df = 1, p-value = 0.09898
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.00641871  0.07197371
sample estimates:
   prop 1    prop 2 
0.1632653 0.1304878

ggplot(totalapaUnexplained_sig,aes(x=loc)) + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(y="Proportion", title = "Total apaQTLs explaining eQTLs")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

totalapaUnexplained_sig_loc= totalapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocTotalUn=n()) %>% mutate(propTotalUn=nLocTotalUn/nrow(totalapaUnexplained_sig))
totalapaexplained_sig_loc= totalapaexplained_sig %>% group_by(loc) %>% summarise(nLocTotalEx=n()) %>% mutate(propTotalEx=nLocTotalEx/nrow(totalapaexplained_sig))

BothTotalLoc=totalapaUnexplained_sig_loc %>% full_join(totalapaexplained_sig_loc,by="loc") %>%  replace_na(list(propTotalUn = 0, nLocTotalUn = 0,propTotalEx=0,nLocTotalEx=0  ))

BothTotalLoc

# A tibble: 5 x 5
  loc    nLocTotalUn propTotalUn nLocTotalEx propTotalEx
  <chr>        <dbl>       <dbl>       <dbl>       <dbl>
1 cds              7      0.0729           8      0.0748
2 end              9      0.0938           7      0.0654
3 intron          17      0.177           20      0.187 
4 utr3            59      0.615           70      0.654 
5 utr5             4      0.0417           2      0.0187

nuclear

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

nuclearapaUnexplained_sig= nuclearapaUnexplained %>% filter(adjPval<.05)

nrow(nuclearapaUnexplained_sig)/nrow(nuclearapaUnexplained)

[1] 0.1649832

I tested 594 unexplained eQTLs in the nuclear fraction and 98 have a bonforoni corrected significant peak.

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))

nuclearapaexplained_sig= nuclearapaexplained %>% filter(adjPval<.05)

nrow(nuclearapaexplained_sig)/nrow(nuclearapaexplained)

[1] 0.13269

I tested 829 explained eQTLs in the nuclear fraction and 110 have a nominally significant peak. difference of proportions:

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(nuclearapaexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(nuclearapaexplained))
X-squared = 2.6386, df = 1, p-value = 0.1043
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.006890426  0.071476780
sample estimates:
   prop 1    prop 2 
0.1649832 0.1326900

ggplot(nuclearapaUnexplained_sig,aes(x=loc))  + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(title = "Nuclear apaQTLs explaining eQTLs", y="Proportion")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

nuclearapaUnexplained_sig_loc= nuclearapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearUn=n()) %>% mutate(propnuclearUn=nLocnuclearUn/nrow(nuclearapaUnexplained_sig))
nuclearapaexplained_sig_loc= nuclearapaexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearEx=n()) %>% mutate(propnuclearEx=nLocnuclearEx/nrow(nuclearapaexplained_sig))

BothnuclearLoc=nuclearapaUnexplained_sig_loc %>% full_join(nuclearapaexplained_sig_loc,by="loc") %>%  replace_na(list(propnuclearUn = 0, nLocnuclearUn = 0,propnuclearEx=0,nLocnuclearEx=0  ))

BothnuclearLoc

# A tibble: 5 x 5
  loc    nLocnuclearUn propnuclearUn nLocnuclearEx propnuclearEx
  <chr>          <dbl>         <dbl>         <dbl>         <dbl>
1 cds                4        0.0408             3        0.0273
2 end               10        0.102              9        0.0818
3 intron            18        0.184             33        0.3   
4 utr3              66        0.673             63        0.573 
5 utr5               0        0                  2        0.0182

prop.test(x=c(18,33), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(18, 33) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 3.1869, df = 1, p-value = 0.07423
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.240913267  0.008260206
sample estimates:
   prop 1    prop 2 
0.1836735 0.3000000

prop.test(x=c(66,63), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(66, 63) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 1.8258, df = 1, p-value = 0.1766
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.03992433  0.24140856
sample estimates:
   prop 1    prop 2 
0.6734694 0.5727273

total v nuclear

prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(totalapaUnexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(totalapaUnexplained)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nuclearapaUnexplained_sig), nrow(totalapaUnexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(totalapaUnexplained))
X-squared = 1.4301e-06, df = 1, p-value = 0.999
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.04220475  0.04564046
sample estimates:
   prop 1    prop 2 
0.1649832 0.1632653

Differences in proportion by location

allLocProp=BothnuclearLoc %>% full_join(BothTotalLoc, by="loc") %>% select(loc,propnuclearUn,propnuclearEx,propTotalUn,propTotalEx )

allLocPropmelt= melt(allLocProp, id.vars = "loc") %>% mutate(Fraction=ifelse(grepl("Total", variable), "Total", "Nuclear"),eQTL=ifelse(grepl("Un", variable), "Unexplained", "Explained"))


ggplot(allLocPropmelt,aes(x=loc, fill=eQTL, y=value)) + geom_histogram(stat="identity", position = "dodge") + facet_grid(~Fraction)+ labs(y="Proportion of PAS", title="apaQTLs overlaping eQTLs by PAS location")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
ca379ce	brimittleman	2019-06-13
b907ac1	brimittleman	2019-06-12

This is a very stringent test. A less stringent way to get an upper bound would be to make an informed decision about which peak to use. This will make it so I am only testing one PAS per gene.

Vary the pvalue cuttoff

To test if .05 is a good cuttoff for this analysis I will create a function that computes the overlap at different cutoffs. I will go from .01 to .5 by .05

totalapaUnexplained totalapaexplained

nuclearapaUnexplained nuclearapaexplained

prop_overlap=function(status, fraction, cutoff){
  if (fraction=="Total"){
    if (status=="Explained"){
      file=totalapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=nrow(sig)/nrow(file)
    }else {
      file=totalapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=nrow(sig)/nrow(file)
    }
  } else{
    if (status=="Explained"){
      file=nuclearapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=nrow(sig)/nrow(file)
     }else {
      file=nuclearapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=nrow(sig)/nrow(file)
     }
  }
  return(proportion)
}

cutoffs=c(0.001,0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.3,0.4,0.5)

TotalExplained_Proportions=c()
for(i in cutoffs){
  TotalExplained_Proportions=c( TotalExplained_Proportions, prop_overlap("Explained", "Total", i))
}
TotalExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Total", 11)))

TotalUnexplained_Proportions=c()
for(i in cutoffs){
  TotalUnexplained_Proportions=c(TotalUnexplained_Proportions, prop_overlap("Unexplained", "Total", i))
}
TotalUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Total", 11)))

NuclearExplained_Proportions=c()
for(i in cutoffs){
  NuclearExplained_Proportions=c( NuclearExplained_Proportions, prop_overlap("Explained", "Nuclear", i))
}
NuclearExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Nuclear", 11)))


NuclearUnexplained_Proportions=c()
for(i in cutoffs){
  NuclearUnexplained_Proportions=c( NuclearUnexplained_Proportions, prop_overlap("Unexplained", "Nuclear", i))
}
NuclearUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Nuclear", 11)))



AllPropDF=bind_rows(TotalExplained_ProportionsDF,TotalUnexplained_ProportionsDF,NuclearExplained_ProportionsDF,NuclearUnexplained_ProportionsDF)

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

AllPropDF$Prop=as.numeric(AllPropDF$Prop)

Plot this:

ggplot(AllPropDF, aes(x=cutoffs, y=Prop, fill=Status)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction)

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] reshape2_1.4.3  workflowr_1.3.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] fansi_0.4.0      readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     utf8_1.1.4       stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Proportion eQTLs explained

Briana Mittleman

6/7/2019

total

nuclear

total v nuclear

Vary the pvalue cuttoff