Nuclear fraction and nascent transcription

Last updated: 2019-09-09

Checks: 6 1

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). 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: objects present

The global environment had objects present when the code in the R Markdown file was run. These objects 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. Use wflow_publish or wflow_build to ensure that the code is always run in an empty environment.

The following objects were defined in the global environment when these results were created:

Name	Class	Size
data	environment	56 bytes
env	environment	56 bytes

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.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: ac31b33

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:    docs/.DS_Store
    Ignored:    docs/figure/.DS_Store
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  .Rprofile
    Untracked:  ._.DS_Store
    Untracked:  .gitignore
    Untracked:  @
    Untracked:  _workflowr.yml
    Untracked:  analysis/._PASdescriptiveplots.Rmd
    Untracked:  analysis/._cuttoffPercUsage.Rmd
    Untracked:  analysis/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom
    Untracked:  analysis/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm.allChrom
    Untracked:  analysis/QTLexampleplots.Rmd
    Untracked:  analysis/cuttoffPercUsage.Rmd
    Untracked:  analysis/eQTLoverlap.Rmd
    Untracked:  analysis/interpret verify bam.Rmd
    Untracked:  analysis/interpret_verifybam.Rmd
    Untracked:  analysis/mergeRNA.Rmd
    Untracked:  analysis/oldstuffNotNeeded.Rmd
    Untracked:  analysis/remove_badlines.Rmd
    Untracked:  analysis/totSpecIncludenotTested.Rmd
    Untracked:  analysis/totalspec.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/.NascentRNAdtPlotFirstintronicPAS.sh.swp
    Untracked:  code/._ApaQTL_nominalNonnorm.sh
    Untracked:  code/._BothFracDTPlotGeneRegions.sh
    Untracked:  code/._BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/._EandPqtl_perm.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/._IntronicPASDT.sh
    Untracked:  code/._LC_samplegroups.py
    Untracked:  code/._LD_qtl.sh
    Untracked:  code/._LD_snpsproxy.sh
    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/._NomResfromPASSNP.py
    Untracked:  code/._NuclearPAS_5per.bed.py
    Untracked:  code/._PTTfacetboxplots.R
    Untracked:  code/._PrematureQTLNominal.sh
    Untracked:  code/._PrematureQTLPermuted.sh
    Untracked:  code/._QTL2bed.py
    Untracked:  code/._QTL2bed_withstrand.py
    Untracked:  code/._RNAbam2bw.sh
    Untracked:  code/._RNAseqDTplot.sh
    Untracked:  code/._RunRes2PAS.sh
    Untracked:  code/._SAF215upbed.py
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilefiltPAS
    Untracked:  code/._TESplots100bp.sh
    Untracked:  code/._TESplots150bp.sh
    Untracked:  code/._TESplots200bp.sh
    Untracked:  code/._TotalPAS_5perc.bed.py
    Untracked:  code/._Untitled
    Untracked:  code/._ZipandTabPheno.sh
    Untracked:  code/._aAPAqtl_nominal39ind.sh
    Untracked:  code/._allNucSpecQTLine.py
    Untracked:  code/._allNucSpecfromNonNorm.py
    Untracked:  code/._annotatePacBioPASregion.sh
    Untracked:  code/._annotatedPAS2bed.py
    Untracked:  code/._apaInPandE.py
    Untracked:  code/._apaQTLCorrectPvalMakeQQ.R
    Untracked:  code/._apaQTLCorrectpval_6or7a.R
    Untracked:  code/._apaQTL_Nominal.sh
    Untracked:  code/._apaQTL_nominalv67.sh
    Untracked:  code/._apaQTL_permuted.sh
    Untracked:  code/._apaQTL_permuted_test6A7A.sh
    Untracked:  code/._apainRibo.py
    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/._changeRibonomQTLres2genename.py
    Untracked:  code/._changenomQTLres2geneName.py
    Untracked:  code/._chooseAnno2PAS_pacbio.py
    Untracked:  code/._chooseAnno2SAF.py
    Untracked:  code/._chooseSignalSite
    Untracked:  code/._chooseSignalSite.py
    Untracked:  code/._closestannotated.sh
    Untracked:  code/._closestannotated_byfrac.sh
    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/._convertNominal2SNPloc2Versions.py
    Untracked:  code/._convertNumeric.py
    Untracked:  code/._correctNomeqtl.R
    Untracked:  code/._createPlinkSampfile.py
    Untracked:  code/._dag.pdf
    Untracked:  code/._eQTL_switch2snploc.py
    Untracked:  code/._eQTLgenestestedapa.py
    Untracked:  code/._encodeRNADTplots.sh
    Untracked:  code/._extractGenotypes.py
    Untracked:  code/._extractseqfromqtlfastq.py
    Untracked:  code/._fc2leafphen.py
    Untracked:  code/._fc_filteredPAS6and7As.sh
    Untracked:  code/._fifteenBPupstreamPAS.py
    Untracked:  code/._fiftyBPupstreamPAS.py
    Untracked:  code/._filter5perc.R
    Untracked:  code/._filter5percPheno.py
    Untracked:  code/._filterLDsnps.py
    Untracked:  code/._filterMPPAS.py
    Untracked:  code/._filterMPPAS_15.py
    Untracked:  code/._filterMPPAS_15_7As.py
    Untracked:  code/._filterMPPAS_50.py
    Untracked:  code/._filterSAFforMP.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/._fixFChead_short.py
    Untracked:  code/._fixH3k12ac.py
    Untracked:  code/._fixPASregionSNPs.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/._getApapval4eqtl_version67.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/._intersectVCFandupPAS.sh
    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/._mapSSsnps2PAS.sh
    Untracked:  code/._mergRNABam.sh
    Untracked:  code/._mergeAllBam.sh
    Untracked:  code/._mergeAnnotations.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/._nucQTLGWAS.py
    Untracked:  code/._nucSpecQTLineData.py
    Untracked:  code/._nucSpeceffectsize.py
    Untracked:  code/._nucspecnucPASine.py
    Untracked:  code/._pQTLsotherdata.py
    Untracked:  code/._pacbioDT.sh
    Untracked:  code/._pacbioIntronicDT.sh
    Untracked:  code/._parseBestbamid.py
    Untracked:  code/._peak2PAS.py
    Untracked:  code/._peakFC.sh
    Untracked:  code/._pheno2countonly.R
    Untracked:  code/._phenoQTLfromlist.py
    Untracked:  code/._processYRIgen.py
    Untracked:  code/._pttQTLsinapaQTL.py
    Untracked:  code/._qtlRegionseq.sh
    Untracked:  code/._qtlsPvalOppFrac.py
    Untracked:  code/._quantassign2parsedpeak.py
    Untracked:  code/._removeXfromHmm.py
    Untracked:  code/._removeloc_pheno.py
    Untracked:  code/._riboQTL.sh
    Untracked:  code/._runCorrectNomEqtl.sh
    Untracked:  code/._runHMMpermuteAPAqtls.sh
    Untracked:  code/._runHMMpermuteeQTLS.sh
    Untracked:  code/._runMakeEmpiricaleQTL_unexp.sh
    Untracked:  code/._runMakeeQTLempirical.sh
    Untracked:  code/._run_bam2bw_all3prime.sh
    Untracked:  code/._run_bam2bw_extra3.sh
    Untracked:  code/._run_bestbamid.sj
    Untracked:  code/._run_filtersnpLD.sh
    Untracked:  code/._run_getAPAfromeQTL_version6.7.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_prxySNP.sh
    Untracked:  code/._run_pttfacetboxplot.sh
    Untracked:  code/._run_sepUsagephen.sh
    Untracked:  code/._run_sepgenobychrom.sh
    Untracked:  code/._run_verifybam.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/._subsetAPAnotEorPgene_2versions.py
    Untracked:  code/._subsetApanoteGene.py
    Untracked:  code/._subsetApanoteGene_2versions.py
    Untracked:  code/._subsetUnexplainedeQTLs.py
    Untracked:  code/._subsetVCF_SS.sh
    Untracked:  code/._subsetVCF_noSSregions.sh
    Untracked:  code/._subsetVCF_upstreamPAS.sh
    Untracked:  code/._subset_diffisopheno.py
    Untracked:  code/._subsetpermAPAwithGenelist.py
    Untracked:  code/._subsetpermAPAwithGenelist_2versions.py
    Untracked:  code/._subsetvcf_otherreg.sh
    Untracked:  code/._subsetvcf_permSS.sh
    Untracked:  code/._subtrachfiveprimeUTR.sh
    Untracked:  code/._subtractExons.sh
    Untracked:  code/._subtractfiveprimeUTR.sh
    Untracked:  code/._tabixSNPS.sh
    Untracked:  code/._tenBPupstreamPAS.py
    Untracked:  code/._testVerifyBam.sh
    Untracked:  code/._totSeceffectsize.py
    Untracked:  code/._twentyBPupstreamPAS.py
    Untracked:  code/._utrdms2saf.py
    Untracked:  code/._vcf2bed.py
    Untracked:  code/._verifyBam18517N.sh
    Untracked:  code/._verifyBam18517T.sh
    Untracked:  code/._verifyBam19128N.sh
    Untracked:  code/._verifyBam19128T.sh
    Untracked:  code/._wrap_verifybam.sh
    Untracked:  code/._writePTTexamplecode.py
    Untracked:  code/._writePTTexamplecode.sh
    Untracked:  code/.pversion
    Untracked:  code/.snakemake/
    Untracked:  code/1
    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_nominal_versions67.err
    Untracked:  code/APAqtl_nominal_versions67.out
    Untracked:  code/APAqtl_permuted.err
    Untracked:  code/APAqtl_permuted.out
    Untracked:  code/APAqtl_permuted_versions67.err
    Untracked:  code/APAqtl_permuted_versions67.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/EandPqtl_perm.sh
    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/IntronicPASDT.err
    Untracked:  code/IntronicPASDT.out
    Untracked:  code/IntronicPASDT.sh
    Untracked:  code/LC_samplegroups.py
    Untracked:  code/LD_qtl.sh
    Untracked:  code/LD_snpsproxy.sh
    Untracked:  code/LD_vcftools.hap.out
    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/NomResfromPASSNP.py
    Untracked:  code/NuclearPAS_5per.bed.py
    Untracked:  code/Nuclear_example.err
    Untracked:  code/Nuclear_example.out
    Untracked:  code/PACbioDT.err
    Untracked:  code/PACbioDT.out
    Untracked:  code/PACbioDTitronic.err
    Untracked:  code/PACbioDTitronic.out
    Untracked:  code/PTTfacetboxplots.R
    Untracked:  code/PrematureQTLNominal.sh
    Untracked:  code/PrematureQTLPermuted.sh
    Untracked:  code/Prematureqtl_nominal.err
    Untracked:  code/Prematureqtl_nominal.out
    Untracked:  code/Prematureqtl_permuted.err
    Untracked:  code/Prematureqtl_permuted.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/RNAseqDTPlotGeneRegions.err
    Untracked:  code/RNAseqDTPlotGeneRegions.out
    Untracked:  code/RNAseqDTplot.sh
    Untracked:  code/Rplots.pdf
    Untracked:  code/RunRes2PAS.sh
    Untracked:  code/SAF215upbed.py
    Untracked:  code/SAF215upbed_gen.py
    Untracked:  code/Script4NuclearPTTqtlexamples.sh
    Untracked:  code/Script4NuclearQTLexamples.sh
    Untracked:  code/Script4TotalPTTqtlexamples.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/TotalPAS_5perc.bed.py
    Untracked:  code/Total_example.err
    Untracked:  code/Total_example.out
    Untracked:  code/Untitled
    Untracked:  code/Upstream100Bases_general.py
    Untracked:  code/YRI_LCL.vcf.gz
    Untracked:  code/YRI_LCL_chr1.vcf.gz.log
    Untracked:  code/YRI_LCL_chr1.vcf.gz.recode.vcf
    Untracked:  code/ZipandTabPheno.sh
    Untracked:  code/aAPAqtl_nominal39ind.sh
    Untracked:  code/allNucSpecQTLine.py
    Untracked:  code/allNucSpecfromNonNorm.py
    Untracked:  code/annotatePacBioPASregion.sh
    Untracked:  code/annotatedPAS2bed.py
    Untracked:  code/annotatedPASregion.err
    Untracked:  code/annotatedPASregion.out
    Untracked:  code/apaInPandE.py
    Untracked:  code/apaQTLCorrectPvalMakeQQ_4pc.R
    Untracked:  code/apaQTLCorrectpval_6or7a.R
    Untracked:  code/apaQTL_Nominal_4pc.sh
    Untracked:  code/apaQTL_nominalv67.sh
    Untracked:  code/apaQTL_permuted.4pc.sh
    Untracked:  code/apaQTL_permuted_test6A7A.sh
    Untracked:  code/apafacetboxplots.R
    Untracked:  code/apainRibo.py
    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/binary_fileset.log
    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/callSHscripts.txt
    Untracked:  code/changePermQTLres2geneName.py
    Untracked:  code/changeRibonomQTLres2genename.py
    Untracked:  code/changenomQTLres2geneName.py
    Untracked:  code/chooseAnno2PAS_pacbio.py
    Untracked:  code/closestannotated.err
    Untracked:  code/closestannotated.out
    Untracked:  code/closestannotated.sh
    Untracked:  code/closestannotated_byfrac.sh
    Untracked:  code/closestannotatedbyfrac.err
    Untracked:  code/closestannotatedbyfrac.out
    Untracked:  code/codingdms2bed.py
    Untracked:  code/convertNominal2SNPLOC.py
    Untracked:  code/convertNominal2SNPloc2Versions.py
    Untracked:  code/correctNomeqtl.R
    Untracked:  code/createPlinkSampfile.py
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/eQTL_switch2snploc.py
    Untracked:  code/eQTLgenestestedapa.py
    Untracked:  code/encodeRNADTplots.sh
    Untracked:  code/environmentLeaf.yaml
    Untracked:  code/extractGenotypes.py
    Untracked:  code/extractseqfromqtlfastq.py
    Untracked:  code/fc2leafphen.py
    Untracked:  code/fc_filteredPAS6and7As.sh
    Untracked:  code/fifteenBPupstreamPAS.py
    Untracked:  code/fiftyBPupstreamPAS.py
    Untracked:  code/filterLDsnps.py
    Untracked:  code/filterMPPAS.py
    Untracked:  code/filterMPPAS_15.py
    Untracked:  code/filterMPPAS_15_7As.py
    Untracked:  code/filterMPPAS_50.py
    Untracked:  code/filterSAFforMP.py
    Untracked:  code/filterSAFforMP_gen.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_short.py
    Untracked:  code/fixFChead_summary.py
    Untracked:  code/fixH3k12ac.py
    Untracked:  code/fixPASregionSNPs.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/getApapval4eqtl_version67.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/intersectPAS_ssSNPS.err
    Untracked:  code/intersectPAS_ssSNPS.out
    Untracked:  code/intersectVCFPAS.err
    Untracked:  code/intersectVCFPAS.out
    Untracked:  code/intersectVCFandupPAS.sh
    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/mapSSsnps2PAS.sh
    Untracked:  code/mergRNABam.sh
    Untracked:  code/mergeAnno.err
    Untracked:  code/mergeAnno.out
    Untracked:  code/mergeAnnotations.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/nucQTLGWAS.py
    Untracked:  code/nucQTLGWAS_withLD.py
    Untracked:  code/nucSpecQTLineData.py
    Untracked:  code/nucSpeceffectsize.py
    Untracked:  code/nucspecnucPASine.py
    Untracked:  code/pQTLsotherdata.py
    Untracked:  code/pacbioDT.sh
    Untracked:  code/pacbioIntronicDT.sh
    Untracked:  code/parseBestbamid.py
    Untracked:  code/phenoQTLfromlist.py
    Untracked:  code/plink.log
    Untracked:  code/processYRIgen.py
    Untracked:  code/prxySNP.err
    Untracked:  code/prxySNP.out
    Untracked:  code/pttFacetBoxplots.err
    Untracked:  code/pttFacetBoxplots.out
    Untracked:  code/pttQTLsinapaQTL.py
    Untracked:  code/pullTwoMechData.py
    Untracked:  code/qtlFacetBoxplots.err
    Untracked:  code/qtlFacetBoxplots.out
    Untracked:  code/qtlRegionseq.sh
    Untracked:  code/qtlsPvalOppFrac.py
    Untracked:  code/rLD_vcftools.hap.err
    Untracked:  code/removeXfromHmm.py
    Untracked:  code/removeloc_pheno.py
    Untracked:  code/riboQTL.sh
    Untracked:  code/riboqtl.err
    Untracked:  code/riboqtl.out
    Untracked:  code/runBestBamID.err
    Untracked:  code/runCorrectNomEqtl.sh
    Untracked:  code/runCorrectNomeqtl.err
    Untracked:  code/runCorrectNomeqtl.out
    Untracked:  code/runFilterLD.err
    Untracked:  code/runFilterLD.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_bam2bw.err
    Untracked:  code/run_bam2bw.out
    Untracked:  code/run_bam2bw_all3prime.sh
    Untracked:  code/run_bam2bw_extra3.sh
    Untracked:  code/run_bam2bwexta.err
    Untracked:  code/run_bam2bwexta.out
    Untracked:  code/run_bestbamid.sh
    Untracked:  code/run_distPAS2Sig.sh
    Untracked:  code/run_filtersnpLD.sh
    Untracked:  code/run_getAPAfromanyeQTL.err
    Untracked:  code/run_getAPAfromanyeQTL.out
    Untracked:  code/run_getAPAfromeQTL_version6.7.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_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_prxySNP.sh
    Untracked:  code/run_pttfacetboxplot.sh
    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/run_verifybam.err
    Untracked:  code/run_verifybam.out
    Untracked:  code/run_verifybam.sh
    Untracked:  code/run_verifybam128N.err
    Untracked:  code/run_verifybam128N.out
    Untracked:  code/run_verifybam128T.err
    Untracked:  code/run_verifybam128T.out
    Untracked:  code/run_verifybam517N.err
    Untracked:  code/run_verifybam517N.out
    Untracked:  code/run_verifybam517T.err
    Untracked:  code/run_verifybam517T.out
    Untracked:  code/run_verifybam_fullVCF.sh
    Untracked:  code/runprxySNP.err
    Untracked:  code/runprxySNP.out
    Untracked:  code/runres2pas.err
    Untracked:  code/runres2pas.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/subsetAPAnotEorPgene_2versions.py
    Untracked:  code/subsetApanoteGene.py
    Untracked:  code/subsetApanoteGene_2versions.py
    Untracked:  code/subsetUnexplainedeQTLs.py
    Untracked:  code/subsetVCF_SS.sh
    Untracked:  code/subsetVCF_noSSregions.sh
    Untracked:  code/subsetVCF_upstreamPAS.sh
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/subsetpermAPAwithGenelist.py
    Untracked:  code/subsetpermAPAwithGenelist_2versions.py
    Untracked:  code/subsetvcf_SS.err
    Untracked:  code/subsetvcf_SS.out
    Untracked:  code/subsetvcf_noSS.err
    Untracked:  code/subsetvcf_noSS.out
    Untracked:  code/subsetvcf_otherreg.sh
    Untracked:  code/subsetvcf_pas.err
    Untracked:  code/subsetvcf_pas.out
    Untracked:  code/subsetvcf_perm.err
    Untracked:  code/subsetvcf_perm.out
    Untracked:  code/subsetvcf_permSS.sh
    Untracked:  code/subsetvcf_rand.err
    Untracked:  code/subsetvcf_rand.out
    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/tenBPupstreamPAS.py
    Untracked:  code/testVerifyBam.sh
    Untracked:  code/test_verifybam.err
    Untracked:  code/test_verifybam.out
    Untracked:  code/totSeceffectsize.py
    Untracked:  code/transcriptdm2bed.py
    Untracked:  code/twentyBPupstreamPAS.py
    Untracked:  code/utrdms2saf.py
    Untracked:  code/vcf2bed.py
    Untracked:  code/vcf_keepsnps.err
    Untracked:  code/vcf_keepsnps.out
    Untracked:  code/verifyBam18517N.sh
    Untracked:  code/verifyBam18517T.sh
    Untracked:  code/verifyBam19128N.sh
    Untracked:  code/verifyBam19128T.sh
    Untracked:  code/wrap_verifybam.err
    Untracked:  code/wrap_verifybam.out
    Untracked:  code/wrap_verifybam.sh
    Untracked:  code/wrap_verifybam_full.sh
    Untracked:  code/writeExampleQTLcode.py
    Untracked:  code/writePTTexamplecode.py
    Untracked:  code/zipandtabPhen.err
    Untracked:  code/zipandtabPhen.out
    Untracked:  data/._.DS_Store
    Untracked:  data/._MetaDataSequencing.txt
    Untracked:  data/AnnotatedPAS/
    Untracked:  data/ApaByEgene/
    Untracked:  data/ApaByPgene/
    Untracked:  data/BadLines/
    Untracked:  data/Battle_pQTL/
    Untracked:  data/CheckSums/
    Untracked:  data/CompareOldandNew/
    Untracked:  data/DTmatrix/
    Untracked:  data/DiffIso/
    Untracked:  data/EncodeRNA/
    Untracked:  data/ExampleQTLPlots/
    Untracked:  data/FlaggedPAS/
    Untracked:  data/GWAS_overlap/
    Untracked:  data/GeuvadisRNA/
    Untracked:  data/HMMqtls/
    Untracked:  data/Li_eQTLs/
    Untracked:  data/NascentRNA/
    Untracked:  data/NucSpeceQTLeffect/
    Untracked:  data/PAS/
    Untracked:  data/PAS_postFlag/
    Untracked:  data/PolyA_DB/
    Untracked:  data/PreTerm_pheno/
    Untracked:  data/PrematureQTLNominal/
    Untracked:  data/PrematureQTLPermuted/
    Untracked:  data/QTLGenotypes/
    Untracked:  data/QTLoverlap/
    Untracked:  data/QTLoverlap_nonNorm/
    Untracked:  data/README.md
    Untracked:  data/RNAseq/
    Untracked:  data/Reads2UTR/
    Untracked:  data/SNPinSS/
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/TF_motifdisruption/
    Untracked:  data/ThirtyNineIndQtl_nominal/
    Untracked:  data/Version15bp6As/
    Untracked:  data/Version15bp7As/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLNominal_4pc/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/apaQTLPermuted_4pc/
    Untracked:  data/apaQTLs/
    Untracked:  data/assignedPeaks/
    Untracked:  data/assignedPeaks_15Up/
    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/
    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/locusZoom/
    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/oldPASfiles/
    Untracked:  data/overlapeQTL_try2/
    Untracked:  data/overlapeQTLs/
    Untracked:  data/pQTLoverlap/
    Untracked:  data/pacbio/
    Untracked:  data/peakCoverage/
    Untracked:  data/peaks_5perc/
    Untracked:  data/phenotype/
    Untracked:  data/phenotype_5perc/
    Untracked:  data/pttQTL/
    Untracked:  data/pttQTLplots/
    Untracked:  data/sigDiffGenes.txt
    Untracked:  data/sort/
    Untracked:  data/sort_clean/
    Untracked:  data/sort_waspfilter/
    Untracked:  data/twoMech/
    Untracked:  data/verifyBAM/
    Untracked:  data/verifyBAM_full/
    Untracked:  docs/._.DS_Store
    Untracked:  docs/figure/._.DS_Store
    Untracked:  nohup.out
    Untracked:  output/._.DS_Store
    Untracked:  output/._meanCorrelationPhenotypes.svg
    Untracked:  output/dtPlots/
    Untracked:  output/fastqc/
    Untracked:  output/meanCorrelationPhenotypes.svg
    Untracked:  run_verifybam517N.err
    Untracked:  run_verifybam517N.out

Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/signalsiteanalysis.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/HighCrediblePAS.Rmd/figure1bsubset-1.pdf
    Deleted:    reads_graphs.Rmd

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	ac31b33	brimittleman	2019-09-09	add figure for nascent and credible sites
html	22541b3	brimittleman	2019-09-06	Build site.
html	00fe2b4	brimittleman	2019-07-26	Build site.
Rmd	cee6ce0	brimittleman	2019-07-26	get pvalues form <-16 tests
html	1fea2ed	brimittleman	2019-06-25	Build site.
Rmd	8e9e91c	brimittleman	2019-06-25	all genes for first plot
html	9dd4b6e	brimittleman	2019-06-24	Build site.
Rmd	438b5c0	brimittleman	2019-06-24	add npas
html	5b239b1	brimittleman	2019-06-13	Build site.
Rmd	5ea9c06	brimittleman	2019-06-13	fix bug
html	7aeba54	brimittleman	2019-05-17	Build site.
Rmd	78b53a1	brimittleman	2019-05-17	add full apa by loc
html	a295d27	brimittleman	2019-05-16	Build site.
Rmd	75f4567	brimittleman	2019-05-16	add total intron/all
html	460e1fb	brimittleman	2019-05-16	Build site.
Rmd	1df3fe1	brimittleman	2019-05-16	seperate fractions by locations
html	81a3e16	brimittleman	2019-05-15	Build site.
Rmd	f484dcd	brimittleman	2019-05-15	add nascent transcription plot

library(reshape2)
library(workflowr)

This is workflowr version 1.4.0
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(viridis)

Loading required package: viridisLite

Gene name switch file:

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F)

Create transcription phenotype

4su data

FourSU=read.table(file = "../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>%  dplyr::select(gene_id,contains("4su_30"))


FourSU_geneNames=FourSU %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("4su_30"))

FourgeneNames_long=melt(FourSU_geneNames,id.vars = "GeneName", value.name = "FourSU", variable.name = "FourSU_ind") %>% separate(FourSU_ind, into=c("type","time", "1400", "MAf", "Individual"), sep="_") %>% dplyr::select(GeneName, Individual, FourSU) 

FourSU_geneMean=FourgeneNames_long %>% group_by(GeneName) %>%summarise(Mean_4su=mean(FourSU))

rna seq

RNA=read.table(file = "../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>%  dplyr::select(gene_id,contains("RNAseq_14000"))


RNA_geneNames=RNA %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("RNA"))

RNAgeneNames_long=melt(RNA_geneNames,id.vars = "GeneName", value.name = "RNA", variable.name = "RNA_ind") %>%   separate(RNA_ind, into=c("type", "1400", "MAf", "Individual"), sep="_") %>% dplyr::select(GeneName, Individual, RNA) 

RNA_geneMean=RNAgeneNames_long %>% group_by(GeneName) %>%summarise(Mean_RNA=mean(RNA))

Make transcription phenotype

Transcription=FourSU_geneMean %>% inner_join(RNA_geneMean, by="GeneName") %>% mutate(Transcription=Mean_4su/(Mean_4su + Mean_RNA)) %>% dplyr::select(GeneName, Transcription) %>% dplyr::rename("gene"=GeneName)

Transcription2=FourSU_geneMean %>% inner_join(RNA_geneMean, by="GeneName") %>% mutate(Transcription=Mean_4su/Mean_RNA) %>% dplyr::select(GeneName, Transcription) %>% dplyr::rename("gene"=GeneName)

APA phenotype

5 perc apa

peaknumlist=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.5perc.bed", stringsAsFactors = F, header=F, col.names = c("chr", "start","end", "id", "score", "strand"))  %>% separate(id, into=c("peaknum", "geneid"), sep=":") %>% mutate(peakid=paste("peak", peaknum,sep=""))

Restrict to genes with large diff between file:

sig_genes=read.table(file="../data/highdiffsiggenes.txt",col.names = "gene",stringsAsFactors = F)

Nuclear apa

NucAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% dplyr::select(-chrom , -start, -end, -strand, -loc) 


#%>% semi_join(sig_genes, by="gene")

NucApaMelt=melt(NucAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")

#%>% dplyr::select(peakid, gene, Individual, count)


NucAPA_bygene= NucApaMelt %>% group_by(gene,Individual) %>% summarise(NuclearSum=sum(count))

total apa

TotAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% dplyr::select(-chrom , -start, -end, -strand, -loc) 

#%>% semi_join(sig_genes, by="gene")

TotApaMelt=melt(TotAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPA_bygene= TotApaMelt %>% group_by(gene,Individual) %>% summarise(TotalSum=sum(count))

Sum together:

ApaBothFrac=TotAPA_bygene %>% inner_join(NucAPA_bygene, by=c("gene", "Individual"))

ApaBothFrac_melt=melt(ApaBothFrac, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)

Normalize with meta data info:

metadata=read.table("../data/MetaDataSequencing.txt", header = T,stringsAsFactors = F) %>% dplyr::select(line, fraction, Mapped_noMP)

metadata$line= as.character(metadata$line)

ApaBothFracStand=ApaBothFrac_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracStand_geneMean=ApaBothFracStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracStand_geneMean_spread= spread(ApaBothFracStand_geneMean,fraction,meanAPA ) %>% mutate(APAVal=nuclear/(total+ nuclear))

Join data and plot

Density function:

get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

set.seed(1)
dat <- data.frame(
  x = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0, sd = 0.1)
  ),
  y = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0.1, sd = 0.2)
  )
)

Joing apa and transcription

APAandTranscrption= Transcription %>% inner_join(ApaBothFracStand_geneMean_spread, by="gene")
APAandTranscrption$density <- get_density(APAandTranscrption$APAVal, APAandTranscrption$Transcription, n = 100)



summary(lm(data=APAandTranscrption, APAVal~Transcription))


Call:
lm(formula = APAVal ~ Transcription, data = APAandTranscrption)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36495 -0.10143  0.00799  0.10823  0.38633 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.391266   0.007299   53.61   <2e-16 ***
Transcription 0.268111   0.013614   19.69   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1343 on 7879 degrees of freedom
Multiple R-squared:  0.04691,   Adjusted R-squared:  0.04679 
F-statistic: 387.8 on 1 and 7879 DF,  p-value: < 2.2e-16

cor.test(x=APAandTranscrption$Transcription,y=APAandTranscrption$APAVal)$p.value

[1] 2.560137e-84

Plot:

ggplot(APAandTranscrption, aes(x=Transcription, y=APAVal))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="Nuclear/Nuclear+Total", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13

Split Nuclear by intronic

Nuclear intronic:

I will have to change the gene names for the 3’ info:

NucAPAIntron=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="intron")%>% dplyr::select(-chrom , -start, -end, -strand, -loc)

NucApaIntronMelt=melt(NucAPAIntron, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


NucAPAIntron_bygene= NucApaIntronMelt %>% group_by(gene,Individual) %>% summarise(NuclearIntronSum=sum(count))

Total UTR

TotUTRAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>%filter(loc=="utr3") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

TotApaUTRMelt=melt(TotUTRAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPAUTR_bygene= TotApaUTRMelt %>% group_by(gene,Individual) %>% summarise(TotalUTRSum=sum(count))

ApaBothFracLoc=TotAPAUTR_bygene %>% inner_join(NucAPAIntron_bygene, by=c("gene", "Individual"))

ApaBothFracLoc_melt=melt(ApaBothFracLoc, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalUTRSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)


ApaBothFracLocStand=ApaBothFracLoc_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracLocStand_geneMean=ApaBothFracLocStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracLocStand_geneMean_spread= spread(ApaBothFracLocStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/(total+nuclear)) 

ApaBothFracLocStand_geneMean_spread2= spread(ApaBothFracLocStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/total)

Join this with the transcription info:

APAlocationandTranscrption= Transcription %>% inner_join(ApaBothFracLocStand_geneMean_spread, by="gene")
APAlocationandTranscrption$density <- get_density(APAlocationandTranscrption$APAValLoc, APAlocationandTranscrption$Transcription, n = 100)

ggplot(APAlocationandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="NuclearIntron/TotalUTR + IntronNuclear", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
5b239b1	brimittleman	2019-06-13

summary(lm(data=APAlocationandTranscrption, APAValLoc~Transcription))


Call:
lm(formula = APAValLoc ~ Transcription, data = APAlocationandTranscrption)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3265 -0.1894 -0.0686  0.1418  0.7199 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.13634    0.02166   6.294 3.49e-10 ***
Transcription  0.30874    0.03889   7.939 2.74e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2353 on 3401 degrees of freedom
Multiple R-squared:  0.0182,    Adjusted R-squared:  0.01791 
F-statistic: 63.03 on 1 and 3401 DF,  p-value: 2.743e-15

Just the ratio:

APAlocationandTranscrption2= Transcription2 %>% inner_join(ApaBothFracLocStand_geneMean_spread2, by="gene")
APAlocationandTranscrption2$density <- get_density(APAlocationandTranscrption2$APAValLoc, APAlocationandTranscrption2$Transcription, n = 100)
summary(lm(data=APAlocationandTranscrption2, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAlocationandTranscrption2)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.56425 -0.42530 -0.04272  0.37494  2.55395 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.51548    0.01116 -46.208   <2e-16 ***
log10(Transcription)  0.49074    0.05312   9.238   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5932 on 3401 degrees of freedom
Multiple R-squared:  0.02448,   Adjusted R-squared:  0.02419 
F-statistic: 85.34 on 1 and 3401 DF,  p-value: < 2.2e-16

ggplot(APAlocationandTranscrption2, aes(x=log10(Transcription), y=log10(APAValLoc)))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(4su/RNA)", y="log10(NuclearIntron/TotalUTR)", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
5b239b1	brimittleman	2019-06-13

Compare nuclear and total UTR

NucAPAUTR=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="utr3")%>% dplyr::select(-chrom , -start, -end, -strand, -loc)

NucAPAUTRMelt=melt(NucAPAUTR, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


NucAPAUTR_bygene= NucAPAUTRMelt %>% group_by(gene,Individual) %>% summarise(NuclearUTRSum=sum(count))

ApaBothFracUTR=TotAPAUTR_bygene %>% inner_join(NucAPAUTR_bygene, by=c("gene", "Individual"))

ApaBothFracUTR_melt=melt(ApaBothFracUTR, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalUTRSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)


ApaBothFracUTRStand=ApaBothFracUTR_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracUTRStand_geneMean=ApaBothFracUTRStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracUTRStand_geneMean_spread= spread(ApaBothFracUTRStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/total)
ApaBothFracUTRStand_geneMean_spread2= spread(ApaBothFracUTRStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/(total+nuclear))

THis is nuclear vs total only looking at teh UTR:

APAUTRandTranscrption= Transcription %>% inner_join(ApaBothFracUTRStand_geneMean_spread, by="gene")
APAUTRandTranscrption$density <- get_density(APAUTRandTranscrption$APAValLoc, APAUTRandTranscrption$Transcription, n = 100)


summary(lm(data=APAUTRandTranscrption, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAUTRandTranscrption)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.70017 -0.18961 -0.00315  0.19567  0.91050 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.152115   0.009048   16.81   <2e-16 ***
log10(Transcription) 0.486492   0.029505   16.49   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2518 on 7709 degrees of freedom
Multiple R-squared:  0.03407,   Adjusted R-squared:  0.03394 
F-statistic: 271.9 on 1 and 7709 DF,  p-value: < 2.2e-16

ggplot(APAUTRandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/RNA", y="NuclearUTR/TotalUTR", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
5b239b1	brimittleman	2019-06-13
460e1fb	brimittleman	2019-05-16

APAUTRandTranscrption2= Transcription %>% inner_join(ApaBothFracUTRStand_geneMean_spread2, by="gene")
APAUTRandTranscrption2$density <- get_density(APAUTRandTranscrption2$APAValLoc, APAUTRandTranscrption2$Transcription, n = 100)


summary(lm(data=APAUTRandTranscrption2, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAUTRandTranscrption2)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45859 -0.08654  0.01628  0.10173  0.37084 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.235769   0.004561   -51.7   <2e-16 ***
log10(Transcription)  0.269145   0.014872    18.1   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1269 on 7709 degrees of freedom
Multiple R-squared:  0.04075,   Adjusted R-squared:  0.04063 
F-statistic: 327.5 on 1 and 7709 DF,  p-value: < 2.2e-16

ggplot(APAUTRandTranscrption2, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/RNA+4su", y="NuclearUTR/TotalUTR+NuclearUTR", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
5b239b1	brimittleman	2019-06-13

Intron Nuclear over all nuclear

Nuclear intron= NucAPAIntron_bygene

all nuclear =NucAPA_bygene

Create this pheno:

ApaNuclear_byloc=NucAPAIntron_bygene %>% inner_join(NucAPA_bygene, by=c("gene", "Individual")) %>% mutate(IntronOverAll=NuclearIntronSum/NuclearSum) %>% mutate(fraction="nuclear",line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, IntronOverAll) %>% group_by(gene) %>% filter(IntronOverAll!=0) %>%  summarise(MeanIntronoverAll=mean(IntronOverAll)) %>% dplyr::rename("GeneName"=gene)

Join with RNA

nuclearandRNA=ApaNuclear_byloc %>% inner_join(RNA_geneMean, by="GeneName")

nuclearandRNA$density <- get_density(nuclearandRNA$MeanIntronoverAll, nuclearandRNA$Mean_RNA, n = 100)

Plot:

ggplot(nuclearandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13

summary(lm(data=nuclearandRNA, MeanIntronoverAll~log10(Mean_RNA)))


Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = nuclearandRNA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.34199 -0.15170 -0.05155  0.11068  0.80839 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.234585   0.033994  -6.901 6.11e-12 ***
log10(Mean_RNA) -0.125270   0.007938 -15.780  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.203 on 3489 degrees of freedom
Multiple R-squared:  0.06662,   Adjusted R-squared:  0.06635 
F-statistic:   249 on 1 and 3489 DF,  p-value: < 2.2e-16

Same plot with transcription phenotype on bottom:

ApaNuclear_byloc_rename=ApaNuclear_byloc %>% dplyr::rename("gene"=GeneName)
nuclearandtranscription=ApaNuclear_byloc_rename %>% inner_join(Transcription, by="gene")

nuclearandtranscription$density <- get_density(nuclearandtranscription$MeanIntronoverAll, nuclearandtranscription$Transcription, n = 100)

ggplot(nuclearandtranscription, aes(x=Transcription, y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13

summary(lm(data=nuclearandtranscription, MeanIntronoverAll~Transcription))


Call:
lm(formula = MeanIntronoverAll ~ Transcription, data = nuclearandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.28328 -0.16653 -0.05979  0.11668  0.70545 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.29220    0.01902  15.366   <2e-16 ***
Transcription  0.01262    0.03416   0.369    0.712    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2101 on 3489 degrees of freedom
Multiple R-squared:  3.91e-05,  Adjusted R-squared:  -0.0002475 
F-statistic: 0.1364 on 1 and 3489 DF,  p-value: 0.7119

Intron Total over all Total

First I need to get the total intronic:

TotAPAIntron=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>%filter(loc=="intron") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

TotAPAIntronMelt=melt(TotAPAIntron, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPAIntron_bygene= TotAPAIntronMelt %>% group_by(gene,Individual) %>% summarise(TotalIntronSum=sum(count))

ApaTotal_byloc=TotAPAIntron_bygene %>% inner_join(TotAPA_bygene, by=c("gene", "Individual")) %>% mutate(IntronOverAll=TotalIntronSum/TotalSum) %>% mutate(fraction="total",line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, IntronOverAll) %>% group_by(gene) %>% filter(IntronOverAll!=0) %>%  summarise(MeanIntronoverAll=mean(IntronOverAll)) %>% dplyr::rename("GeneName"=gene)

Join with RNA

totalandRNA=ApaTotal_byloc %>% inner_join(RNA_geneMean, by="GeneName")

totalandRNA$density <- get_density(totalandRNA$MeanIntronoverAll, totalandRNA$Mean_RNA, n = 100)

Plot:

ggplot(totalandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13

summary(lm(data=totalandRNA, MeanIntronoverAll~log10(Mean_RNA)))


Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = totalandRNA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.31597 -0.11142 -0.05084  0.05668  0.91908 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.387446   0.029256  -13.24   <2e-16 ***
log10(Mean_RNA) -0.137665   0.006832  -20.15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1747 on 3489 degrees of freedom
Multiple R-squared:  0.1042,    Adjusted R-squared:  0.104 
F-statistic:   406 on 1 and 3489 DF,  p-value: < 2.2e-16

Same plot with transcription phenotype on bottom:

ApaTotal_byloc_rename=ApaTotal_byloc %>% dplyr::rename("gene"=GeneName)
totalandtranscription=ApaTotal_byloc_rename %>% inner_join(Transcription, by="gene")

totalandtranscription$density <- get_density(totalandtranscription$MeanIntronoverAll, totalandtranscription$Transcription, n = 100)

ggplot(totalandtranscription, aes(x=Transcription, y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13
7aeba54	brimittleman	2019-05-17

summary(lm(data=totalandtranscription, MeanIntronoverAll~Transcription))


Call:
lm(formula = MeanIntronoverAll ~ Transcription, data = totalandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.19479 -0.12577 -0.06159  0.06326  0.80891 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.18698    0.01671  11.193   <2e-16 ***
Transcription  0.02206    0.03001   0.735    0.462    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1846 on 3489 degrees of freedom
Multiple R-squared:  0.0001549, Adjusted R-squared:  -0.0001316 
F-statistic: 0.5407 on 1 and 3489 DF,  p-value: 0.4622

Put it all together:

(Nuclear intronic/nuclear all)/(total intronic/total all) vs 4su/(4su+RNA)

Nucintron v nuc all:

ApaNuclear_byloc_rename

ApaTotal_byloc_rename

Transcription

fullapa=ApaNuclear_byloc_rename %>% dplyr::rename("NuclearIntronoverall"=MeanIntronoverAll)%>% inner_join(ApaTotal_byloc_rename, by="gene") %>% mutate(fullAPA=NuclearIntronoverall/MeanIntronoverAll) %>% dplyr::select(gene,fullAPA)


#join with transcription 


BothlocPhenoandtranscription=fullapa %>% inner_join(Transcription, by="gene")

BothlocPhenoandtranscription$density <- get_density(BothlocPhenoandtranscription$fullAPA, BothlocPhenoandtranscription$Transcription, n = 100)

ggplot(BothlocPhenoandtranscription, aes(x=Transcription, y=fullAPA))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="log10(NuclearIntron/Nuclearall)/(TotalIntron/TotalAll)", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06
1fea2ed	brimittleman	2019-06-25
5b239b1	brimittleman	2019-06-13

summary(lm(data=BothlocPhenoandtranscription, log10(fullAPA)~Transcription))


Call:
lm(formula = log10(fullAPA) ~ Transcription, data = BothlocPhenoandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.04512 -0.15891 -0.00646  0.15119  0.97338 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.36597    0.02186  16.741  < 2e-16 ***
Transcription -0.23538    0.03927  -5.995 2.25e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2416 on 3489 degrees of freedom
Multiple R-squared:  0.01019,   Adjusted R-squared:  0.009911 
F-statistic: 35.94 on 1 and 3489 DF,  p-value: 2.248e-09

Does number of PAS correlate with nascent transcription

I want to look at number of PAS in the nuclear fraction against 4su/4su+RNA. This is the transcription phenotype in this analysis

nPASnuc=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc", header = F, stringsAsFactors = F, col.names = c("chr", "start", "end","gene", "loc","strand", "peakNum", "meanusage")) %>% group_by(gene) %>% summarise(nPASN=n())

transcriptionPAS=Transcription %>%inner_join(nPASnuc, by="gene")


transcriptionPAS$density <- get_density(transcriptionPAS$nPASN, transcriptionPAS$Transcription, n = 100)



ggplot(transcriptionPAS, aes(x=Transcription, y=nPASN)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Number of PAS", title="Number of Nuclear PAS increases as nascent transcription increases")

Version	Author	Date
22541b3	brimittleman	2019-09-06
9dd4b6e	brimittleman	2019-06-24

summary(lm(data=transcriptionPAS, nPASN~Transcription))


Call:
lm(formula = nPASN ~ Transcription, data = transcriptionPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8031 -1.2709 -0.3399  0.9833  8.3229 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.30831    0.09171   3.362 0.000779 ***
Transcription  4.96629    0.17108  29.030  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.687 on 7879 degrees of freedom
Multiple R-squared:  0.09662,   Adjusted R-squared:  0.09651 
F-statistic: 842.7 on 1 and 7879 DF,  p-value: < 2.2e-16

Try for total fraction:

nPAStot=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc", header = F, stringsAsFactors = F, col.names = c("chr", "start", "end","gene", "loc","strand", "peakNum", "meanusage")) %>% group_by(gene) %>% summarise(nPAST=n())

transcriptionTOTPAS=Transcription %>%inner_join(nPAStot, by="gene")


transcriptionTOTPAS$density <- get_density(transcriptionTOTPAS$nPAST, transcriptionTOTPAS$Transcription, n = 100)



ggplot(transcriptionTOTPAS, aes(x=Transcription, y=nPAST)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Number of PAS", title="Number of Total PAS increases as nascent transcription increases")

Version	Author	Date
22541b3	brimittleman	2019-09-06
9dd4b6e	brimittleman	2019-06-24

summary(lm(data=transcriptionTOTPAS, nPAST~Transcription))


Call:
lm(formula = nPAST ~ Transcription, data = transcriptionTOTPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2809 -1.0575 -0.2933  0.7959  7.0142 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.14210    0.07792   1.824   0.0682 .  
Transcription  4.57287    0.14534  31.462   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.434 on 7879 degrees of freedom
Multiple R-squared:  0.1116,    Adjusted R-squared:  0.1115 
F-statistic: 989.9 on 1 and 7879 DF,  p-value: < 2.2e-16

Look at the difference in PAS number:

nPASall= nPAStot %>% inner_join(nPASnuc, by="gene") %>% mutate(Difference= nPASN-nPAST) 

transcriptionallPAS=Transcription %>%inner_join(nPASall, by="gene")

transcriptionallPAS$density <- get_density(transcriptionallPAS$Difference, transcriptionallPAS$Transcription, n = 100)

ggplot(transcriptionallPAS, aes(x=Transcription, y=Difference)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Diffference in number of PAS", title="Number of Total PAS increases as nascent transcription increases")

Version	Author	Date
22541b3	brimittleman	2019-09-06
9dd4b6e	brimittleman	2019-06-24

summary(lm(data=transcriptionallPAS, Difference~Transcription))


Call:
lm(formula = Difference ~ Transcription, data = transcriptionallPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3758 -0.3925 -0.3446  0.5983  5.6461 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.16621    0.05165   3.218   0.0013 ** 
Transcription  0.39342    0.09634   4.084 4.48e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9503 on 7879 degrees of freedom
Multiple R-squared:  0.002112,  Adjusted R-squared:  0.001985 
F-statistic: 16.68 on 1 and 7879 DF,  p-value: 4.476e-05

This has a small positive correlation.

RNA decay

Do this with Athma’s decay.

decay=read.table(file = "../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>% dplyr::select(gene_id,contains("RNAdecay"))

decay_geneNames=decay %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("RNAdecay"))

decay_geneNames_long=melt(decay_geneNames,id.vars = "GeneName", value.name = "RNA_Decay", variable.name = "Decay_Ind") %>% separate(Decay_Ind, into=c("type", "ind"), sep="_") %>% mutate(line=paste("NA" , ind, sep="")) %>% dplyr::select(GeneName, line, RNA_Decay) %>% dplyr::rename( "gene"=GeneName)

APAandDecay=decay_geneNames_long %>% inner_join(ApaBothFrac_melt, by=c('gene', 'line'))
ngenes=APAandDecay %>% dplyr::select(gene) %>% unique() %>% nrow()
ngenes

[1] 7881

summary(lm(data=APAandDecay, APA_val~RNA_Decay))


Call:
lm(formula = APA_val ~ RNA_Decay, data = APAandDecay)

Residuals:
   Min     1Q Median     3Q    Max 
  -849   -454   -356   -145 809821 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  512.257      4.058  126.24   <2e-16 ***
RNA_Decay    388.772     24.162   16.09   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3492 on 756574 degrees of freedom
Multiple R-squared:  0.0003421, Adjusted R-squared:  0.0003407 
F-statistic: 258.9 on 1 and 756574 DF,  p-value: < 2.2e-16

ngenes=APAandDecay %>% dplyr::select(gene) %>% unique() %>% nrow()
ngenes

[1] 7881

decay_byGene= decay_geneNames_long %>% group_by(gene) %>% summarise(MeanDecay=mean(RNA_Decay))
APAandDecayMean=decay_byGene %>% inner_join(ApaBothFracStand_geneMean_spread, by=c('gene'))



APAandDecayMean$density <- get_density(APAandDecayMean$APAVal, APAandDecayMean$MeanDecay, n = 100)

ggplot(APAandDecayMean, aes(x=MeanDecay, y=APAVal)) + geom_point(aes(color=density)) + geom_smooth(method="lm") + labs(x="relative Decay", y="Nuclear/(Total+Nuclear)", title="Relationship between Nuclear proportion and RNA decay")+ scale_color_viridis()

Version	Author	Date
22541b3	brimittleman	2019-09-06

summary(lm(data=APAandDecayMean, APAVal~MeanDecay))


Call:
lm(formula = APAVal ~ MeanDecay, data = APAandDecayMean)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36888 -0.10414  0.00977  0.11337  0.35047 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.530569   0.001577 336.413  < 2e-16 ***
MeanDecay   -0.054487   0.012607  -4.322 1.56e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1374 on 7879 degrees of freedom
Multiple R-squared:  0.002365,  Adjusted R-squared:  0.002239 
F-statistic: 18.68 on 1 and 7879 DF,  p-value: 1.564e-05

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] viridis_0.5.1     viridisLite_0.3.0 forcats_0.3.0    
 [4] stringr_1.3.1     dplyr_0.8.0.1     purrr_0.3.2      
 [7] readr_1.3.1       tidyr_0.8.3       tibble_2.1.1     
[10] ggplot2_3.1.1     tidyverse_1.2.1   workflowr_1.4.0  
[13] reshape2_1.4.3   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] pillar_1.3.1     glue_1.3.0       withr_2.1.2      modelr_0.1.2    
[13] readxl_1.1.0     plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[17] cellranger_1.1.0 rvest_0.3.2      evaluate_0.12    labeling_0.3    
[21] knitr_1.20       highr_0.7        broom_0.5.1      Rcpp_1.0.0      
[25] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[29] gridExtra_2.3    hms_0.4.2        digest_0.6.18    stringi_1.2.4   
[33] grid_3.5.1       rprojroot_1.3-2  cli_1.1.0        tools_3.5.1     
[37] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.25.2     compiler_3.5.1

Nuclear fraction and nascent transcription

Briana Mittleman

5/15/2019

Create transcription phenotype

APA phenotype

Join data and plot

Split Nuclear by intronic

Nuclear intronic:

Total UTR

Compare nuclear and total UTR

Intron Nuclear over all nuclear

Same plot with transcription phenotype on bottom:

Intron Total over all Total

Same plot with transcription phenotype on bottom:

Put it all together:

Does number of PAS correlate with nascent transcription

RNA decay