Last updated: 2019-06-10

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: c753b24

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:    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/cuttoffPercUsage.Rmd
    Untracked:  analysis/eQTLoverlap.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/.NascentRNAdtPlotFirstintronicPAS.sh.swp
    Untracked:  code/._ApaQTL_nominalNonnorm.sh
    Untracked:  code/._BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/._FC_NucintornUpandDown.sh
    Untracked:  code/._FC_UTR.sh
    Untracked:  code/._FC_intornUpandDownsteamPAS.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/._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/._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/._convertNumeric.py
    Untracked:  code/._correctNomeqtl.R
    Untracked:  code/._dag.pdf
    Untracked:  code/._eQTLgenestestedapa.py
    Untracked:  code/._encodeRNADTplots.sh
    Untracked:  code/._extractGenotypes.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/._getUpstreamIntronNuclear.py
    Untracked:  code/._grouptranscripts.py
    Untracked:  code/._keepSNP_vcf.sh
    Untracked:  code/._make5percPeakbed.py
    Untracked:  code/._makeFileID.py
    Untracked:  code/._makePheno.py
    Untracked:  code/._makeSAFbothfrac5perc.py
    Untracked:  code/._makeeQTLempirical_unexp.py
    Untracked:  code/._makeeQTLempiricaldist.py
    Untracked:  code/._makegencondeTSSfile.py
    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/._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_getapapval4eqtl_unexp.sh
    Untracked:  code/._run_leafcutterDiffIso.sh
    Untracked:  code/._run_sepUsagephen.sh
    Untracked:  code/._selectNominalPvalues.py
    Untracked:  code/._sepUsagePhen.py
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._submit-snakemakePAS.sh
    Untracked:  code/._submit-snakemakefiltPAS.sh
    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/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_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/README.md
    Untracked:  code/Rplots.pdf
    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/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/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/codingdms2bed.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/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/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/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/keepSNP_vcf.sh
    Untracked:  code/log/
    Untracked:  code/makeSAFbothfrac5perc.py
    Untracked:  code/makeeQTLempirical_unexp.py
    Untracked:  code/makeeQTLempiricaldist.py
    Untracked:  code/makegencondeTSSfile.py
    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/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/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_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_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_sepusage.err
    Untracked:  code/run_sepusage.out
    Untracked:  code/selectNominalPvalues.py
    Untracked:  code/sepUsagePhen.py
    Untracked:  code/seqQTLregion.err
    Untracked:  code/seqQTLregion.out
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    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/zipandtabPhen.err
    Untracked:  code/zipandtabPhen.out
    Untracked:  data/ApaByEgene/
    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/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/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/DiffIsoAnalysis.Rmd
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/exvunexpeQTL.Rmd
    Modified:   analysis/motifDisruption.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/config.yaml
    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 c753b24 brimittleman 2019-06-10 add nuc res
html 09323e7 brimittleman 2019-06-07 Build site.
Rmd f640ca3 brimittleman 2019-06-07 add results
html 4fa86e5 brimittleman 2019-06-07 Build site.
Rmd 3e5cc8e brimittleman 2019-06-07 code up to FC

I am interested in finding examples of intronic PAS that show RNAseq signatures upstream of the PAS but not downstream. To do this I will create a ratio of reads upstream/reads downstream standardized by the length of the region (up/downstream).

To do this I can use the work I did previously. Here I assigned each intronic PAS to an intron. I will do this analysis with the total fraction because I will be looking at steady state RNA seq.

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()

Total

mkdir ../data/intronRNAratio
totIntronicPeaks=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc", stringsAsFactors = F, header = F,col.names = c("chr", "start", "end", "gene", "loc", "strand", "peak", "avgUsage")) %>% filter(loc=="intron") 

pas2intronTot=read.table("../data/intron_analysis/TotalIntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand"))  %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronCHR,intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage)

write.table(pas2intronTot, "../data/intronRNAratio/TotalIntronicPAS2Intron.txt", quote = F, row.names = F, col.names = F, sep="\t")

Make upstream and downstream PAS saf files using python.

 python getIntronUpstreamPAS.py
 python getIntronDownstreamPAS.py

These make Bed and SAF files. I will use the SAF files for feature counts with all of the RNA seq. These files are in /project2/yangili1/LCL/RNAseqGeuvadisBams/*.final.bam

sbatch FC_intornUpandDownsteamPAS.sh

Downstream Results:

downstream=read.table("../data/intronRNAratio/DownstreamIntron.fc", header = T,stringsAsFactors = F)
downstreamMean=rowSums(downstream[,7:ncol(downstream)])

downstreanMeanDF=as.data.frame(cbind(downstream[,1:6], downstreamMean)) %>% mutate(DownstreamMean_st=downstreamMean/Length) %>% select(Geneid,DownstreamMean_st )

Upstream Results:

upstream=read.table("../data/intronRNAratio/UpstreamIntron.fc", header = T,stringsAsFactors = F)
upstreamMean=rowSums(upstream[,7:ncol(upstream)])
upstreamMeanDF=as.data.frame(cbind(upstream[,1:6], upstreamMean)) %>% mutate(UpstreamMean_st=upstreamMean/Length) %>% select(Geneid,UpstreamMean_st )

Join Results:

I will use upstream - downstream instead of a ratio to deal with 0s

UpandDown=upstreamMeanDF %>% inner_join(downstreanMeanDF, by="Geneid") %>% mutate(ratio=UpstreamMean_st-DownstreamMean_st) %>% arrange(desc(ratio)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage"))

summary(UpandDown$ratio)
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-26617.062     -0.103      0.000     -2.511      0.236   5380.027

I want to know how many are positive:

MoreUp=UpandDown %>% filter(ratio>0) 
summary(MoreUp$ratio)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
   0.000    0.064    0.346    7.631    2.044 5380.027 
nrow(MoreUp)
[1] 13903

13903 examples where there are more reads upstream in the intron than downstream.

To look for examples:

head(MoreUp)
         PAS   gene    loc    PASloc             Usage UpstreamMean_st
1 peak113671  RPS18 intron  33240934 0.110925925925926        5404.273
2  peak56811  RPL27 intron  41152263 0.796296296296296        3739.972
3  peak78512 RPL37A intron 217365085 0.407407407407407        3284.307
4  peak70756 RPS27A intron  55460229 0.537037037037037        3233.041
5 peak113669  RPS18 intron  33240357 0.537222222222222        2942.865
6  peak20394  RPLP2 intron    810791 0.648148148148148        1683.759
  DownstreamMean_st    ratio
1         24.246212 5380.027
2          7.555006 3732.417
3         15.007150 3269.300
4         15.651852 3217.389
5        369.229167 2573.636
6         25.031017 1658.728

Nuclear

We also have nacent RNA seq. I will do this with nuclear intronic PAS.

nucIntronicPeaks=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc", stringsAsFactors = F, header = F,col.names = c("chr", "start", "end", "gene", "loc", "strand", "peak", "avgUsage")) %>% filter(loc=="intron")


pas2intronNuc=read.table("../data/intron_analysis/IntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand"))  %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronCHR,intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage)

write.table(pas2intronTot, "../data/intronRNAratio/NuclearIntronicPAS2Intron.txt", quote = F, row.names = F, col.names = F, sep="\t")

Make upstream and downstream PAS saf files using python.

python getUpstreamIntronNuclear.py
python getDownstreamIntronNuclear.py

These make Bed and SAF files. I will use the SAF files for feature counts with all of the nacent RNA seq.

sbatch FC_NucintornUpandDown.sh

Downstream Results:

downstreamNuc=read.table("../data/intronRNAratio/NuclearDownstreamIntron.fc", col.names = c("Geneid","Chr","Start", "End", "Strand", "Length", "Downstream"), header = T,stringsAsFactors = F)%>% mutate(Downstream_st=Downstream/Length) %>% select(Geneid,Downstream_st )

upstreamNuc=read.table("../data/intronRNAratio/NuclearUpstreamIntron.fc", col.names = c("Geneid","Chr","Start", "End", "Strand", "Length", "Upstream"), header = T,stringsAsFactors = F)%>% mutate(Upstream_st=Upstream/Length) %>% select(Geneid,Upstream_st )


UpandDown_nuc=upstreamNuc %>% inner_join(downstreamNuc, by="Geneid") %>% mutate(ratio=Upstream_st-Downstream_st) %>% arrange(desc(ratio)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage"))

summary(UpandDown_nuc$ratio)
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-1120.4860    -0.1540     0.0000    -0.1154     0.3679    57.5287 
MoreUpNuc=UpandDown_nuc %>% filter(ratio>0) 
summary(MoreUpNuc$ratio)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00001  0.13175  0.39811  0.86737  0.96318 57.52867 
nrow(MoreUpNuc)
[1] 14913
head(MoreUpNuc)
         PAS    gene    loc    PASloc             Usage Upstream_st
1  peak18198   BTAF1 intron  93719017 0.836666666666667    68.64486
2 peak113043   HLA-A intron  29911562 0.296296296296296    56.75309
3  peak32403 HSP90B1 intron 104335869 0.302037037037037    40.16410
4  peak86087   DDX17 intron  38887949 0.781851851851852    89.22321
5  peak69710 RASGRP3 intron  33741824  0.32462962962963    38.86842
6  peak92554  PARP14 intron 122414653 0.087037037037037    32.15172
  Downstream_st    ratio
1     11.116190 57.52867
2     23.910979 32.84211
3      8.178899 31.98520
4     57.814883 31.40833
5      7.689828 31.17859
6      3.907366 28.24436

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

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] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4