PAS descriptive plots

Last updated: 2019-06-13

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: 02b669e

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:    analysis/figure/
    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/oldstuffNotNeeded.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/._QTL2bed.py
    Untracked:  code/._QTL2bed_withstrand.py
    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/._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/._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/._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/._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/QTL2bed.py
    Untracked:  code/QTL2bed_withstrand.py
    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/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/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/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/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/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/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/mapapaQTL.Rmd
    Modified:   analysis/motifDisruption.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.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/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	02b669e	brimittleman	2019-06-13	fix big bug
html	0dbb65b	brimittleman	2019-05-22	Build site.
Rmd	d3df9c3	brimittleman	2019-05-22	fix plots
html	6af55a2	brimittleman	2019-05-21	Build site.
Rmd	2fd13bb	brimittleman	2019-05-21	add location plots
html	a88eedf	brimittleman	2019-05-20	Build site.
html	ccebe33	brimittleman	2019-04-24	Build site.
html	74a1372	brimittleman	2019-04-24	Build site.
html	012892d	brimittleman	2019-04-24	Build site.
html	1fb7086	brimittleman	2019-04-23	Build site.

In this analysis I will create discriptive plots for the PAS identified in the 54 LCLs.

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(reshape2)


Attaching package: 'reshape2'

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

    smiths

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Peaks per gene:

I want to plot how many genes have 0, 1, 2 and more than 2 PAS in the set. I need to join my PAS with the annotation to find out how many genes have 0 PAS.

pas=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", header = F, stringsAsFactors = F, col.names = c("Chr", "start", "end", "PeakID", "score", "strand")) %>% separate(PeakID, into=c("peaknum", "geneAnno"), sep=":") %>% separate(geneAnno, into=c("Gene", "Loc"),sep="_")

pasbygene= pas %>% group_by(Gene) %>% summarise(PAS=n())

annotation=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed", col.names = c("chr", "start", "end", "anno", "score", "strand")) %>% separate(anno, into=c("Loc", "Gene"),sep=":") %>% group_by(Gene) %>% summarise(annos=n()) %>% dplyr::select(Gene)

PASallgene=annotation %>% full_join(pasbygene, by="Gene") %>% replace_na(list(PAS=0)) 

#group with 0,1,2,more than 2
PASallgene_grouped=PASallgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))

Plot this:

Genes=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
PAS=c("Zero", "One", "Multiple")
AllPAS=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
GenebyPAS=as.data.frame(cbind(PAS,AllPAS))
GenebyPAS$AllPAS=as.numeric(as.character(GenebyPAS$AllPAS))

allPASplot=ggplot(GenebyPAS, aes(x="",y=AllPAS, fill=PAS)) + geom_bar(stat="identity", width=.5) + scale_fill_brewer(palette="GnBu") + labs(title="Identified PAS per Gene", y="Genes",x="All Indentified PAS")
allPASplot

Version	Author	Date
74a1372	brimittleman	2019-04-24
012892d	brimittleman	2019-04-24
1fb7086	brimittleman	2019-04-23

ggsave(allPASplot, file="../output/GeneswithAPApotentialAllPAS.png", width=3, height=5)

Subset and get stats for UTR

pasUTR=pas %>% filter(Loc=="utr3") %>% group_by(Gene) %>% summarise(PAS=n())

pasUTR_allgene=annotation %>% full_join(pasUTR, by="Gene") %>% replace_na(list(PAS=0)) 

PASUTRallgene_grouped=pasUTR_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))


GenesUTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
UTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
GenebyPASUTR=as.data.frame(cbind(PAS,UTR))
GenebyPASUTR$UTR=as.numeric(as.character(GenebyPASUTR$UTR))

ggplot(GenebyPASUTR, aes(x="",y=UTR, fill=PAS)) + geom_bar(stat="identity")

Version	Author	Date
74a1372	brimittleman	2019-04-24
012892d	brimittleman	2019-04-24
1fb7086	brimittleman	2019-04-23

Subset and get stats for Intron

pasIntron=pas %>% filter(Loc=="intron" | Loc=='utr3') %>% group_by(Gene) %>% summarise(PAS=n())

pasIntron_allgene=annotation %>% full_join(pasIntron, by="Gene") %>% replace_na(list(PAS=0)) 

pasIntronallgene_grouped=pasIntron_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))


UTRandIntron=c(sum(pasIntronallgene_grouped$Zero),sum(pasIntronallgene_grouped$One),sum(pasIntronallgene_grouped$Multiple))
GenebyPASIntron=as.data.frame(cbind(PAS,UTRandIntron))
GenebyPASIntron$UTRandIntron=as.numeric(as.character(GenebyPASIntron$UTRandIntron))

ggplot(GenebyPASIntron, aes(x="",y=UTRandIntron, fill=PAS)) + geom_bar(stat="identity")

Version	Author	Date
74a1372	brimittleman	2019-04-24
012892d	brimittleman	2019-04-24
1fb7086	brimittleman	2019-04-23

Make these side by side:

GenebyPASUTR_melt=melt(GenebyPASUTR, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPAS_melt=melt(GenebyPAS, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPASIntron_melt=melt(GenebyPASIntron, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPAStoplot=rbind(GenebyPAS_melt,GenebyPASUTR_melt,GenebyPASIntron_melt)

geneswithAPA=ggplot(GenebyPAStoplot, aes(x=Set,y=Genes, fill=PAS, by=Set)) + geom_bar(stat="identity")+ scale_fill_brewer(palette="YlGnBu") + labs(title="Genes with APA poential")

geneswithAPA

Version	Author	Date
74a1372	brimittleman	2019-04-24
012892d	brimittleman	2019-04-24
1fb7086	brimittleman	2019-04-23

ggsave(geneswithAPA, file="../output/GeneswithAPApotential.png")

Saving 7 x 5 in image

GenebyPAStoplot

       PAS          Set Genes
1     Zero       AllPAS 11659
2      One       AllPAS  5361
3 Multiple       AllPAS 10095
4     Zero          UTR 14594
5      One          UTR  7479
6 Multiple          UTR  5042
7     Zero UTRandIntron 13089
8      One UTRandIntron  5677
9 Multiple UTRandIntron  8349

Location of PAS

PAS_loc=pas %>% group_by(Loc) %>% summarise(nPAS=n())
loclabel=c("Coding", "Downstream", "Intronic", "3' UTR", "5' UTR")
PASLocPlot=ggplot(PAS_loc, aes(x=Loc, y=nPAS, fill=Loc)) + geom_bar(stat="identity",width=.5)+ scale_fill_brewer(palette = "YlGnBu") + labs(x="Gene location", y="Number of identified PAS", title="Location distribution for identified PAS") + theme(legend.position = "none")+ scale_x_discrete(labels= loclabel)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
PASLocPlot

Version	Author	Date
74a1372	brimittleman	2019-04-24
012892d	brimittleman	2019-04-24

ggsave(PASLocPlot, file="../output/PASlocation.png")

Saving 7 x 5 in image

Number of genes with apa by cutoff

I want to make a script that takes a cuttoff and tells me how many gens have 0,1, >1 PAS. This way I can put these together to make stacked barplots:

I can make the plot for total then again for nuclear.

The annotaiton is annotation:

totapaanno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc",header = T,stringsAsFactors = F) 
indiv=colnames(totapaanno)[2:55]
totapanum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric",header = F, col.names = indiv) 
totapa_mean=rowMeans(totapanum)
totapaMeananno=as.data.frame(cbind(ID=totapaanno$chrom, meanUsage=totapa_mean))
totapaMeananno$meanUsage=as.numeric(as.character(totapaMeananno$meanUsage))
totapaMeananno$ID=as.character(totapaMeananno$ID)

genesbycuttoff_tot=function(fraction){
  totapaMeananno_filt=totapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(Gene) %>% summarise(PAS=n())
  PASallgene=annotation %>% full_join(totapaMeananno_filt, by="Gene") %>% replace_na(list(PAS=0))
  PASallgene_cat=PASallgene %>% mutate(Category=ifelse(PAS==0,"Zero", ifelse(PAS==1, "One", "Multiple"))) %>% group_by(Category) %>% summarise(NPer=n())
return(PASallgene_cat$NPer)
}

#multiple, one, zero 
categories=c("Multiple_PAS", "One_PAS", "Zero_PAS")
FullDF=as.data.frame(cbind(categories))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs[1:10])
{
FullDF=cbind(FullDF,val=genesbycuttoff_tot(val))
}

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [4801,
4802, 4803].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3549,
3550, 3551].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2951,
2952].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2573,
2574].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2269].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2024].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1827].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1651].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1521].

colnames(FullDF)=c("Category",cutoffs[1:10])

Melt:

fullDF_melt=melt(FullDF,id.vars = "Category",variable.name = "Cutoff", value.name = "NGenes") %>% mutate(propGene=NGenes/nrow(annotation))

ggplot(fullDF_melt,aes(x=Cutoff, y=propGene, by=Category, fill=Category)) + geom_bar( stat="identity",width = .5) + scale_fill_brewer(palette="GnBu") + labs(title="Proportion of genes with Multiple PAS by Total Usage filter",y="Proportion of 27115 genes", x="Usage Filter cutoff") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))

Version	Author	Date
0dbb65b	brimittleman	2019-05-22
a88eedf	brimittleman	2019-05-20

Nuclear:

nucapaanno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.fc",header = T,stringsAsFactors = F) 

nucapanum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.CountsOnlyNumeric",header = F, col.names = indiv) 
nucapa_mean=rowMeans(nucapanum)
nucapaMeananno=as.data.frame(cbind(ID=nucapaanno$chrom, meanUsage=nucapa_mean))
nucapaMeananno$meanUsage=as.numeric(as.character(nucapaMeananno$meanUsage))
nucapaMeananno$ID=as.character(nucapaMeananno$ID)

genesbycuttoff_nuc=function(fraction){
  nucapaMeananno_filt=nucapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(Gene) %>% summarise(PAS=n())
  PASallgene=annotation %>% full_join(nucapaMeananno_filt, by="Gene") %>% replace_na(list(PAS=0))
  PASallgene_cat=PASallgene %>% mutate(Category=ifelse(PAS==0,"Zero", ifelse(PAS==1, "One", "Multiple"))) %>% group_by(Category) %>% summarise(NPer=n())
return(PASallgene_cat$NPer)
}

#multiple, one, zero 
FullDFNuc=as.data.frame(cbind(categories))
for (val in cutoffs[1:10])
{
FullDFNuc=cbind(FullDFNuc,val=genesbycuttoff_nuc(val))
}

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [5399,
5400, 5401, 5402].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3814,
3815, 3816].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [3076,
3077].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2630,
2631].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2310].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2053].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1821].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1645].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1507].

colnames(FullDFNuc)=c("Category",cutoffs[1:10])

FullDFNuc_melt=melt(FullDFNuc,id.vars = "Category",variable.name = "Cutoff", value.name = "NGenes") %>% mutate(propGene=NGenes/nrow(annotation))

ggplot(FullDFNuc_melt,aes(x=Cutoff, y=propGene, by=Category, fill=Category)) + geom_bar( stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="Proportion of genes with Multiple PAS by Nuclear Usage filter",y="Proportion of 27115 genes", x="Usage Filter cutoff")

Version	Author	Date
a88eedf	brimittleman	2019-05-20

Location of PAS by filter

I will make these plots but the categories will be location of the PAS.

locbycutoff_tot=function(fraction){
  totapaMeananno_filt=totapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(loc) %>% summarise(PerLoc=n()) %>%filter(loc!= "008559")
return(totapaMeananno_filt$PerLoc)
}

locations=c("cds", "end", "intron", "utr3", "utr5")
FullDF_loc=as.data.frame(cbind(locations))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs[1:10])
{
FullDF_loc=cbind(FullDF_loc,val=locbycutoff_tot(val))
}

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [4801,
4802, 4803].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3549,
3550, 3551].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2951,
2952].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2573,
2574].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2269].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2024].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1827].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1651].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1521].

colnames(FullDF_loc)=c("Location",cutoffs[1:10])

Melt:

FullDF_loc_melt=melt(FullDF_loc,id.vars = "Location",variable.name = "Cutoff", value.name = "NPas") %>% group_by(Cutoff) %>%  mutate(propPAS=NPas/sum(NPas))

totplotloc=ggplot(FullDF_loc_melt,aes(x=Cutoff, y=propPAS, by=Location, fill=Location)) + geom_bar(width=.5, stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="PAS location\n Total Fraction",y="Proportion of PAS", x="Usage Filter cutoff")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))

Nuclear

locbycutoff_nuc=function(fraction){
nucapaMeananno_filt=nucapaMeananno %>% filter(meanUsage >=fraction) %>% separate(ID, into=c("chrom", "start","end", "peakID"),sep=":") %>% separate(peakID, into=c("Gene","loc", "strand", "peak"),sep="_") %>% group_by(loc) %>% summarise(PerLoc=n()) %>%filter(loc!= "008559")
return(nucapaMeananno_filt$PerLoc)
}

NucFullDF_loc=as.data.frame(cbind(locations))
cutoffs=seq(from=0, to=.5, by=.05)
for (val in cutoffs)
{
NucFullDF_loc=cbind(NucFullDF_loc,val=locbycutoff_nuc(val))
}

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].

Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [5399,
5400, 5401, 5402].

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [3814,
3815, 3816].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [3076,
3077].

Warning: Expected 4 pieces. Additional pieces discarded in 2 rows [2630,
2631].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2310].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [2053].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1821].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1645].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1507].

Warning: Expected 4 pieces. Additional pieces discarded in 1 rows [1366].

colnames(NucFullDF_loc)=c("Location",cutoffs)

Melt:

NucFullDF_locMelt=melt(NucFullDF_loc,id.vars = "Location",variable.name = "Cutoff", value.name = "NPas") %>% group_by(Cutoff) %>%  mutate(propPAS=NPas/sum(NPas))

nucplotloc=ggplot(NucFullDF_locMelt,aes(x=Cutoff, y=propPAS, by=Location, fill=Location)) + geom_bar(width = .5, stat="identity") + scale_fill_brewer(palette="GnBu") + labs(title="PAS location\n Nuclear Fraction",y="Proportion of PAS", x="Usage Filter cutoff")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete(name="Usage Filter cutoff", breaks=c("0","0.1","0.2", "0.3", "0.4","0.5"))

Plot next to eachother

plot_grid(nucplotloc, totplotloc)

Version	Author	Date
0dbb65b	brimittleman	2019-05-22
6af55a2	brimittleman	2019-05-21

totplotloc

Version	Author	Date
0dbb65b	brimittleman	2019-05-22

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] cowplot_0.9.4   reshape2_1.4.3  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 workflowr_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] pillar_1.3.1       compiler_3.5.1     git2r_0.25.2      
 [7] plyr_1.8.4         tools_3.5.1        digest_0.6.18     
[10] lubridate_1.7.4    jsonlite_1.6       evaluate_0.12     
[13] nlme_3.1-137       gtable_0.2.0       lattice_0.20-38   
[16] pkgconfig_2.0.2    rlang_0.3.1        cli_1.0.1         
[19] rstudioapi_0.10    yaml_2.2.0         haven_1.1.2       
[22] withr_2.1.2        xml2_1.2.0         httr_1.3.1        
[25] knitr_1.20         hms_0.4.2          generics_0.0.2    
[28] fs_1.2.6           rprojroot_1.3-2    grid_3.5.1        
[31] tidyselect_0.2.5   glue_1.3.0         R6_2.3.0          
[34] readxl_1.1.0       rmarkdown_1.10     modelr_0.1.2      
[37] magrittr_1.5       whisker_0.3-2      backports_1.1.2   
[40] scales_1.0.0       htmltools_0.3.6    rvest_0.3.2       
[43] assertthat_0.2.0   colorspace_1.3-2   labeling_0.3      
[46] stringi_1.2.4      lazyeval_0.2.1     munsell_0.5.0     
[49] broom_0.5.1        crayon_1.3.4

PAS descriptive plots

Briana Mittleman

4/23/2019

Peaks per gene:

Subset and get stats for UTR

Subset and get stats for Intron

Location of PAS

Number of genes with apa by cutoff

Location of PAS by filter