Conservation Questions
Briana Mittleman
2/28/2020
Last updated: 2020-04-23
Checks: 7 0
Knit directory: Comparative_APA/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190902) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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: code/chimp_log/
Ignored: code/human_log/
Ignored: data/.DS_Store
Ignored: data/TrialFiltersMeta.txt.sb-9845453e-R58Y0Q/
Ignored: data/mediation_prot/
Ignored: data/metadata_HCpanel.txt.sb-284518db-RGf0kd/
Ignored: data/metadata_HCpanel.txt.sb-a5794dd2-i594qs/
Ignored: output/.DS_Store
Untracked files:
Untracked: ._.DS_Store
Untracked: Chimp/
Untracked: Human/
Untracked: analysis/AREstabilityScores.Rmd
Untracked: analysis/CrossChimpThreePrime.Rmd
Untracked: analysis/DiffTransProtvsExpression.Rmd
Untracked: analysis/DiffUsedUTR.Rmd
Untracked: analysis/GvizPlots.Rmd
Untracked: analysis/HandC.TvN
Untracked: analysis/PhenotypeOverlap10.Rmd
Untracked: analysis/annotationBias.Rmd
Untracked: analysis/assessReadQual.Rmd
Untracked: analysis/diffExpressionPantro6.Rmd
Untracked: code/._AlignmentScores.sh
Untracked: code/._BothFCMM.sh
Untracked: code/._BothFCMMPrim.sh
Untracked: code/._BothFCnewOInclusive.sh
Untracked: code/._ChimpStarMM2.sh
Untracked: code/._ClassifyLeafviz.sh
Untracked: code/._ClosestorthoEx.sh
Untracked: code/._Config_chimp.yaml
Untracked: code/._Config_chimp_full.yaml
Untracked: code/._Config_human.yaml
Untracked: code/._ConvertJunc2Bed.sh
Untracked: code/._CountNucleotides.py
Untracked: code/._CrossMapChimpRNA.sh
Untracked: code/._CrossMapThreeprime.sh
Untracked: code/._DiffSplice.sh
Untracked: code/._DiffSplicePlots.sh
Untracked: code/._DiffSplicePlots_gencode.sh
Untracked: code/._DiffSplice_gencode.sh
Untracked: code/._DiffSplice_removebad.sh
Untracked: code/._Filter255MM.sh
Untracked: code/._FilterPrimSec.sh
Untracked: code/._FindIntronForDomPAS.sh
Untracked: code/._FindIntronForDomPAS_DF.sh
Untracked: code/._GetMAPQscore.py
Untracked: code/._GetSecondaryMap.py
Untracked: code/._Lift5perPAS.sh
Untracked: code/._LiftFinalChimpJunc2Human.sh
Untracked: code/._LiftOrthoPAS2chimp.sh
Untracked: code/._MapBadSamples.sh
Untracked: code/._MismatchNumbers.sh
Untracked: code/._PAS_ATTAAA.sh
Untracked: code/._PAS_ATTAAA_df.sh
Untracked: code/._PAS_seqExpanded.sh
Untracked: code/._PASsequences.sh
Untracked: code/._PASsequences_DF.sh
Untracked: code/._PlotNuclearUsagebySpecies.R
Untracked: code/._PlotNuclearUsagebySpecies_DF.R
Untracked: code/._QuantMergedClusters.sh
Untracked: code/._RNATranscriptDTplot.sh
Untracked: code/._ReverseLiftFilter.R
Untracked: code/._RunFixLeafCluster.sh
Untracked: code/._RunNegMCMediation.sh
Untracked: code/._RunNegMCMediationDF.sh
Untracked: code/._RunPosMCMediationDF.err
Untracked: code/._RunPosMCMediationDF.sh
Untracked: code/._SAF2Bed.py
Untracked: code/._Snakefile
Untracked: code/._SnakefilePAS
Untracked: code/._SnakefilePASfilt
Untracked: code/._SortIndexBadSamples.sh
Untracked: code/._StarMM2.sh
Untracked: code/._TestFC.sh
Untracked: code/._assignPeak2Intronicregion
Untracked: code/._assignPeak2Intronicregion.sh
Untracked: code/._bed215upbed.py
Untracked: code/._bed2Bedbothstrand.py
Untracked: code/._bed2SAF_gen.py
Untracked: code/._buildIndecpantro5
Untracked: code/._buildIndecpantro5.sh
Untracked: code/._buildLeafviz.sh
Untracked: code/._buildLeafviz_leadAnno.sh
Untracked: code/._buildStarIndex.sh
Untracked: code/._chimpChromprder.sh
Untracked: code/._chimpMultiCov.sh
Untracked: code/._chimpMultiCov255.sh
Untracked: code/._chimpMultiCovInclusive.sh
Untracked: code/._chooseSignalSite.py
Untracked: code/._cleanbed2saf.py
Untracked: code/._cluster.json
Untracked: code/._cluster2bed.py
Untracked: code/._clusterLiftReverse.sh
Untracked: code/._clusterLiftReverse_removebad.sh
Untracked: code/._clusterLiftprimary.sh
Untracked: code/._clusterLiftprimary_removebad.sh
Untracked: code/._converBam2Junc.sh
Untracked: code/._converBam2Junc_removeBad.sh
Untracked: code/._extraSnakefiltpas
Untracked: code/._extractPhyloReg.py
Untracked: code/._extractPhyloRegGene.py
Untracked: code/._extractPhylopGeneral.ph
Untracked: code/._extractPhylopGeneral.py
Untracked: code/._extractPhylopReg200down.py
Untracked: code/._extractPhylopReg200up.py
Untracked: code/._filter5percPAS.py
Untracked: code/._filterNumChroms.py
Untracked: code/._filterPASforMP.py
Untracked: code/._filterPostLift.py
Untracked: code/._filterPrimaryread.py
Untracked: code/._filterSecondaryread.py
Untracked: code/._fixExonFC.py
Untracked: code/._fixFCheadforExp.py
Untracked: code/._fixLeafCluster.py
Untracked: code/._fixLiftedJunc.py
Untracked: code/._fixUTRexonanno.py
Untracked: code/._formathg38Anno.py
Untracked: code/._formatpantro6Anno.py
Untracked: code/._getRNAseqMapStats.sh
Untracked: code/._hg19MapStats.sh
Untracked: code/._humanChromorder.sh
Untracked: code/._humanMultiCov.sh
Untracked: code/._humanMultiCov255.sh
Untracked: code/._humanMultiCov_inclusive.sh
Untracked: code/._intersectLiftedPAS.sh
Untracked: code/._liftJunctionFiles.sh
Untracked: code/._liftPAS19to38.sh
Untracked: code/._liftedchimpJunc2human.sh
Untracked: code/._makeNuclearDapaplots.sh
Untracked: code/._makeNuclearDapaplots_DF.sh
Untracked: code/._makeSamplyGroupsHuman_TvN.py
Untracked: code/._mapRNAseqhg19.sh
Untracked: code/._mapRNAseqhg19_newPipeline.sh
Untracked: code/._maphg19.sh
Untracked: code/._maphg19_subjunc.sh
Untracked: code/._mediation_test.R
Untracked: code/._mergeChimp3prime_inhg38.sh
Untracked: code/._mergeandBWRNAseq.sh
Untracked: code/._mergedBam2BW.sh
Untracked: code/._nameClusters.py
Untracked: code/._negativeMediation_montecarlo.R
Untracked: code/._negativeMediation_montecarloDF.R
Untracked: code/._numMultimap.py
Untracked: code/._overlapMMandOrthoexon.sh
Untracked: code/._overlapPASandOrthoexon.sh
Untracked: code/._overlapapaQTLPAS.sh
Untracked: code/._parseHg38.py
Untracked: code/._postiveMediation_montecarlo_DF.R
Untracked: code/._prepareCleanLiftedFC_5perc4LC.py
Untracked: code/._prepareLeafvizAnno.sh
Untracked: code/._preparePAS4lift.py
Untracked: code/._primaryLift.sh
Untracked: code/._processhg38exons.py
Untracked: code/._quantJunc.sh
Untracked: code/._quantJunc_TEST.sh
Untracked: code/._quantJunc_removeBad.sh
Untracked: code/._quantLiftedPASPrimary.sh
Untracked: code/._quantMerged_seperatly.sh
Untracked: code/._recLiftchim2human.sh
Untracked: code/._revLiftPAShg38to19.sh
Untracked: code/._reverseLift.sh
Untracked: code/._runCheckReverseLift.sh
Untracked: code/._runChimpDiffIso.sh
Untracked: code/._runCountNucleotides.sh
Untracked: code/._runFilterNumChroms.sh
Untracked: code/._runHumanDiffIso.sh
Untracked: code/._runNuclearDiffIso_DF.sh
Untracked: code/._runNuclearDifffIso.sh
Untracked: code/._runTotalDiffIso.sh
Untracked: code/._run_chimpverifybam.sh
Untracked: code/._run_verifyBam.sh
Untracked: code/._snakemake.batch
Untracked: code/._snakemakePAS.batch
Untracked: code/._snakemakePASchimp.batch
Untracked: code/._snakemakePAShuman.batch
Untracked: code/._snakemake_chimp.batch
Untracked: code/._snakemake_human.batch
Untracked: code/._snakemakefiltPAS.batch
Untracked: code/._snakemakefiltPAS_chimp
Untracked: code/._snakemakefiltPAS_chimp.sh
Untracked: code/._snakemakefiltPAS_human.sh
Untracked: code/._spliceSite2Fasta.py
Untracked: code/._submit-snakemake-chimp.sh
Untracked: code/._submit-snakemake-human.sh
Untracked: code/._submit-snakemakePAS-chimp.sh
Untracked: code/._submit-snakemakePAS-human.sh
Untracked: code/._submit-snakemakefiltPAS-chimp.sh
Untracked: code/._submit-snakemakefiltPAS-human.sh
Untracked: code/._subset_diffisopheno_Nuclear_HvC.py
Untracked: code/._subset_diffisopheno_Nuclear_HvC_DF.py
Untracked: code/._subset_diffisopheno_Total_HvC.py
Untracked: code/._threeprimeOrthoFC.sh
Untracked: code/._transcriptDTplotsNuclear.sh
Untracked: code/._verifyBam4973.sh
Untracked: code/._verifyBam4973inHuman.sh
Untracked: code/._wrap_chimpverifybam.sh
Untracked: code/._wrap_verifyBam.sh
Untracked: code/._writeMergecode.py
Untracked: code/.snakemake/
Untracked: code/ALLPAS_sequenceDF.err
Untracked: code/ALLPAS_sequenceDF.out
Untracked: code/AlignmentScores.err
Untracked: code/AlignmentScores.out
Untracked: code/AlignmentScores.sh
Untracked: code/BothFCMM.err
Untracked: code/BothFCMM.out
Untracked: code/BothFCMM.sh
Untracked: code/BothFCMMPrim.err
Untracked: code/BothFCMMPrim.out
Untracked: code/BothFCMMPrim.sh
Untracked: code/BothFCnewOInclusive.sh
Untracked: code/BothFCnewOInclusive.sh.err
Untracked: code/BothFCnewOInclusive.sh.out
Untracked: code/ChimpStarMM2.err
Untracked: code/ChimpStarMM2.out
Untracked: code/ChimpStarMM2.sh
Untracked: code/ClassifyLeafviz.sh
Untracked: code/ClosestorthoEx.err
Untracked: code/ClosestorthoEx.out
Untracked: code/ClosestorthoEx.sh
Untracked: code/Config_chimp.yaml
Untracked: code/Config_chimp_full.yaml
Untracked: code/Config_human.yaml
Untracked: code/ConvertJunc2Bed.err
Untracked: code/ConvertJunc2Bed.out
Untracked: code/ConvertJunc2Bed.sh
Untracked: code/CountNucleotides.py
Untracked: code/CrossMapChimpRNA.sh
Untracked: code/CrossMapThreeprime.sh
Untracked: code/CrossmapChimp3prime.err
Untracked: code/CrossmapChimp3prime.out
Untracked: code/CrossmapChimpRNA.err
Untracked: code/CrossmapChimpRNA.out
Untracked: code/DTUTR.sh
Untracked: code/DiffSplice.err
Untracked: code/DiffSplice.out
Untracked: code/DiffSplice.sh
Untracked: code/DiffSplicePlots.err
Untracked: code/DiffSplicePlots.out
Untracked: code/DiffSplicePlots.sh
Untracked: code/DiffSplicePlots_gencode.sh
Untracked: code/DiffSplice_gencode.sh
Untracked: code/DiffSplice_removebad.err
Untracked: code/DiffSplice_removebad.out
Untracked: code/DiffSplice_removebad.sh
Untracked: code/Filter255.err
Untracked: code/Filter255.out
Untracked: code/Filter255MM.sh
Untracked: code/FilterPrimSec.err
Untracked: code/FilterPrimSec.out
Untracked: code/FilterPrimSec.sh
Untracked: code/FilterReverseLift.err
Untracked: code/FilterReverseLift.out
Untracked: code/FindDomXCutoff.py
Untracked: code/FindIntronForDomPAS.err
Untracked: code/FindIntronForDomPAS.out
Untracked: code/FindIntronForDomPAS.sh
Untracked: code/FindIntronForDomPAS_DF.sh
Untracked: code/GencodeDiffSplice.err
Untracked: code/GencodeDiffSplice.out
Untracked: code/GetMAPQscore.py
Untracked: code/GetSecondaryMap.py
Untracked: code/GetTopminus2Usage.py
Untracked: code/HchromOrder.err
Untracked: code/HchromOrder.out
Untracked: code/IntersectMMandOrtho.err
Untracked: code/IntersectMMandOrtho.out
Untracked: code/IntersectPASandOrtho.err
Untracked: code/IntersectPASandOrtho.out
Untracked: code/JunctionLift.err
Untracked: code/JunctionLift.out
Untracked: code/JunctionLiftFinalChimp.err
Untracked: code/JunctionLiftFinalChimp.out
Untracked: code/Lift5perPAS.sh
Untracked: code/Lift5perPASbed.err
Untracked: code/Lift5perPASbed.out
Untracked: code/LiftClustersFirst.err
Untracked: code/LiftClustersFirst.out
Untracked: code/LiftClustersFirst_remove.err
Untracked: code/LiftClustersFirst_remove.out
Untracked: code/LiftClustersSecond.err
Untracked: code/LiftClustersSecond.out
Untracked: code/LiftClustersSecond_remove.err
Untracked: code/LiftClustersSecond_remove.out
Untracked: code/LiftFinalChimpJunc2Human.sh
Untracked: code/LiftOrthoPAS2chimp.sh
Untracked: code/LiftorthoPAS.err
Untracked: code/LiftorthoPASt.out
Untracked: code/Log.out
Untracked: code/MapBadSamples.err
Untracked: code/MapBadSamples.out
Untracked: code/MapBadSamples.sh
Untracked: code/MapStats.err
Untracked: code/MapStats.out
Untracked: code/MaxEntCode/
Untracked: code/MergeClusters.err
Untracked: code/MergeClusters.out
Untracked: code/MergeClusters.sh
Untracked: code/MismatchNumbers.err
Untracked: code/MismatchNumbers.out
Untracked: code/MismatchNumbers.sh
Untracked: code/NuclearDTUTR.err
Untracked: code/NuclearDTUTRt.out
Untracked: code/NuclearPlotsDEandDiffDom_4.err
Untracked: code/NuclearPlotsDEandDiffDom_4.out
Untracked: code/NuclearPlotsDEandDiffDom_4.sh
Untracked: code/PAS_ATTAAA.err
Untracked: code/PAS_ATTAAA.out
Untracked: code/PAS_ATTAAA.sh
Untracked: code/PAS_ATTAAADF.err
Untracked: code/PAS_ATTAAADF.out
Untracked: code/PAS_ATTAAA_df.sh
Untracked: code/PAS_seqExpanded.sh
Untracked: code/PAS_sequence.err
Untracked: code/PAS_sequence.out
Untracked: code/PAS_sequenceDF.err
Untracked: code/PAS_sequenceDF.out
Untracked: code/PASexpanded_sequenceDF.err
Untracked: code/PASexpanded_sequenceDF.out
Untracked: code/PASsequences.sh
Untracked: code/PASsequences_DF.sh
Untracked: code/PlotNuclearUsagebySpecies.R
Untracked: code/PlotNuclearUsagebySpecies_DF.R
Untracked: code/PlotNuclearUsagebySpecies_DF_DEout.R
Untracked: code/QuantMergeClusters
Untracked: code/QuantMergeClusters.err
Untracked: code/QuantMergeClusters.out
Untracked: code/QuantMergedClusters.sh
Untracked: code/RNATranscriptDTplot.err
Untracked: code/RNATranscriptDTplot.out
Untracked: code/RNATranscriptDTplot.sh
Untracked: code/Rev_liftoverPAShg19to38.err
Untracked: code/Rev_liftoverPAShg19to38.out
Untracked: code/ReverseLiftFilter.R
Untracked: code/RunFixCluster.err
Untracked: code/RunFixCluster.out
Untracked: code/RunFixLeafCluster.sh
Untracked: code/RunNegMCMediation.err
Untracked: code/RunNegMCMediation.sh
Untracked: code/RunNegMCMediationDF.err
Untracked: code/RunNegMCMediationDF.out
Untracked: code/RunNegMCMediationDF.sh
Untracked: code/RunNegMCMediationr.out
Untracked: code/RunNewDom.err
Untracked: code/RunNewDom.out
Untracked: code/RunPosMCMediation.err
Untracked: code/RunPosMCMediation.sh
Untracked: code/RunPosMCMediationDF.err
Untracked: code/RunPosMCMediationDF.out
Untracked: code/RunPosMCMediationDF.sh
Untracked: code/RunPosMCMediationr.out
Untracked: code/SAF215upbed_gen.py
Untracked: code/SAF2Bed.py
Untracked: code/Snakefile
Untracked: code/SnakefilePAS
Untracked: code/SnakefilePASfilt
Untracked: code/SortIndexBadSamples.err
Untracked: code/SortIndexBadSamples.out
Untracked: code/SortIndexBadSamples.sh
Untracked: code/StarMM2.err
Untracked: code/StarMM2.out
Untracked: code/StarMM2.sh
Untracked: code/TestFC.err
Untracked: code/TestFC.out
Untracked: code/TestFC.sh
Untracked: code/TotalTranscriptDTplot.err
Untracked: code/TotalTranscriptDTplot.out
Untracked: code/UTR2FASTA.py
Untracked: code/Upstream10Bases_general.py
Untracked: code/allPASSeq_df.sh
Untracked: code/apaQTLsnake.err
Untracked: code/apaQTLsnake.out
Untracked: code/apaQTLsnakePAS.err
Untracked: code/apaQTLsnakePAS.out
Untracked: code/apaQTLsnakePAShuman.err
Untracked: code/apaQTLsnakefiltPAS.err
Untracked: code/apaQTLsnakefiltPAS.out
Untracked: code/assignPeak2Intronicregion.err
Untracked: code/assignPeak2Intronicregion.out
Untracked: code/assignPeak2Intronicregion.sh
Untracked: code/bam2junc.err
Untracked: code/bam2junc.out
Untracked: code/bam2junc_remove.err
Untracked: code/bam2junc_remove.out
Untracked: code/bed215upbed.py
Untracked: code/bed2Bedbothstrand.py
Untracked: code/bed2SAF_gen.py
Untracked: code/bed2saf.py
Untracked: code/bg_to_cov.py
Untracked: code/buildIndecpantro5
Untracked: code/buildIndecpantro5.sh
Untracked: code/buildLeafviz.err
Untracked: code/buildLeafviz.out
Untracked: code/buildLeafviz.sh
Untracked: code/buildLeafviz_leadAnno.sh
Untracked: code/buildLeafviz_leafanno.err
Untracked: code/buildLeafviz_leafanno.out
Untracked: code/buildStarIndex.sh
Untracked: code/callPeaksYL.py
Untracked: code/chimpChromprder.sh
Untracked: code/chimpMultiCov.err
Untracked: code/chimpMultiCov.out
Untracked: code/chimpMultiCov.sh
Untracked: code/chimpMultiCov255.sh
Untracked: code/chimpMultiCovInclusive.err
Untracked: code/chimpMultiCovInclusive.out
Untracked: code/chimpMultiCovInclusive.sh
Untracked: code/chooseAnno2Bed.py
Untracked: code/chooseAnno2SAF.py
Untracked: code/chooseSignalSite.py
Untracked: code/chromOrder.err
Untracked: code/chromOrder.out
Untracked: code/classifyLeafviz.err
Untracked: code/classifyLeafviz.out
Untracked: code/cleanbed2saf.py
Untracked: code/cluster.json
Untracked: code/cluster2bed.py
Untracked: code/clusterLiftReverse.sh
Untracked: code/clusterLiftReverse_removebad.sh
Untracked: code/clusterLiftprimary.sh
Untracked: code/clusterLiftprimary_removebad.sh
Untracked: code/clusterPAS.json
Untracked: code/clusterfiltPAS.json
Untracked: code/comands2Mege.sh
Untracked: code/converBam2Junc.sh
Untracked: code/converBam2Junc_removeBad.sh
Untracked: code/convertNumeric.py
Untracked: code/environment.yaml
Untracked: code/extraSnakefiltpas
Untracked: code/extractPhyloReg.py
Untracked: code/extractPhyloRegGene.py
Untracked: code/extractPhylopGeneral.py
Untracked: code/extractPhylopReg200down.py
Untracked: code/extractPhylopReg200up.py
Untracked: code/filter5perc.R
Untracked: code/filter5percPAS.py
Untracked: code/filter5percPheno.py
Untracked: code/filterBamforMP.pysam2_gen.py
Untracked: code/filterJuncChroms.err
Untracked: code/filterJuncChroms.out
Untracked: code/filterMissprimingInNuc10_gen.py
Untracked: code/filterNumChroms.py
Untracked: code/filterPASforMP.py
Untracked: code/filterPostLift.py
Untracked: code/filterPrimaryread.py
Untracked: code/filterSAFforMP_gen.py
Untracked: code/filterSecondaryread.py
Untracked: code/filterSortBedbyCleanedBed_gen.R
Untracked: code/filterpeaks.py
Untracked: code/fixExonFC.py
Untracked: code/fixFChead.py
Untracked: code/fixFChead_bothfrac.py
Untracked: code/fixFCheadforExp.py
Untracked: code/fixLeafCluster.py
Untracked: code/fixLiftedJunc.py
Untracked: code/fixUTRexonanno.py
Untracked: code/formathg38Anno.py
Untracked: code/generateStarIndex.err
Untracked: code/generateStarIndex.out
Untracked: code/generateStarIndexHuman.err
Untracked: code/generateStarIndexHuman.out
Untracked: code/getAlloverlap.py
Untracked: code/getRNAseqMapStats.sh
Untracked: code/hg19MapStats.err
Untracked: code/hg19MapStats.out
Untracked: code/hg19MapStats.sh
Untracked: code/humanChromorder.sh
Untracked: code/humanFiles
Untracked: code/humanMultiCov.err
Untracked: code/humanMultiCov.out
Untracked: code/humanMultiCov.sh
Untracked: code/humanMultiCov255.err
Untracked: code/humanMultiCov255.out
Untracked: code/humanMultiCov255.sh
Untracked: code/humanMultiCovInclusive.err
Untracked: code/humanMultiCovInclusive.out
Untracked: code/humanMultiCov_inclusive.sh
Untracked: code/intersectAnno.err
Untracked: code/intersectAnno.out
Untracked: code/intersectAnnoExt.err
Untracked: code/intersectAnnoExt.out
Untracked: code/intersectLiftedPAS.sh
Untracked: code/leafcutter_merge_regtools_redo.py
Untracked: code/liftJunctionFiles.sh
Untracked: code/liftPAS19to38.sh
Untracked: code/liftoverPAShg19to38.err
Untracked: code/liftoverPAShg19to38.out
Untracked: code/log/
Untracked: code/make5percPeakbed.py
Untracked: code/makeFileID.py
Untracked: code/makeNuclearDapaplots.sh
Untracked: code/makeNuclearDapaplots_DF.sh
Untracked: code/makeNuclearPlots.err
Untracked: code/makeNuclearPlots.out
Untracked: code/makeNuclearPlotsDF.err
Untracked: code/makeNuclearPlotsDF.out
Untracked: code/makePheno.py
Untracked: code/makeSamplyGroupsChimp_TvN.py
Untracked: code/makeSamplyGroupsHuman_TvN.py
Untracked: code/mapRNAseqhg19.sh
Untracked: code/mapRNAseqhg19_newPipeline.sh
Untracked: code/maphg19.err
Untracked: code/maphg19.out
Untracked: code/maphg19.sh
Untracked: code/maphg19_new.err
Untracked: code/maphg19_new.out
Untracked: code/maphg19_sub.err
Untracked: code/maphg19_sub.out
Untracked: code/maphg19_subjunc.sh
Untracked: code/mediation_test.R
Untracked: code/merge.err
Untracked: code/mergeChimp3prime_inhg38.sh
Untracked: code/mergeChimpRNA.sh
Untracked: code/merge_leafcutter_clusters_redo.py
Untracked: code/mergeandBWRNAseq.sh
Untracked: code/mergeandsort_ChimpinHuman.err
Untracked: code/mergeandsort_ChimpinHuman.out
Untracked: code/mergedBam2BW.sh
Untracked: code/mergedbam2bw.err
Untracked: code/mergedbam2bw.out
Untracked: code/mergedbamRNAand2bw.err
Untracked: code/mergedbamRNAand2bw.out
Untracked: code/nameClusters.py
Untracked: code/namePeaks.py
Untracked: code/negativeMediation_montecarlo.R
Untracked: code/negativeMediation_montecarloDF.R
Untracked: code/nuclearTranscriptDTplot.err
Untracked: code/nuclearTranscriptDTplot.out
Untracked: code/numMultimap.py
Untracked: code/overlapMMandOrthoexon.sh
Untracked: code/overlapPAS.err
Untracked: code/overlapPAS.out
Untracked: code/overlapPASandOrthoexon.sh
Untracked: code/overlapapaQTLPAS.sh
Untracked: code/overlapapaQTLPAS_extended.sh
Untracked: code/overlapapaQTLPAS_samples.sh
Untracked: code/parseHg38.py
Untracked: code/peak2PAS.py
Untracked: code/pheno2countonly.R
Untracked: code/postiveMediation_montecarlo.R
Untracked: code/postiveMediation_montecarlo_DF.R
Untracked: code/prepareAnnoLeafviz.err
Untracked: code/prepareAnnoLeafviz.out
Untracked: code/prepareCleanLiftedFC_5perc4LC.py
Untracked: code/prepareLeafvizAnno.sh
Untracked: code/preparePAS4lift.py
Untracked: code/prepare_phenotype_table.py
Untracked: code/primaryLift.err
Untracked: code/primaryLift.out
Untracked: code/primaryLift.sh
Untracked: code/processhg38exons.py
Untracked: code/quantJunc.sh
Untracked: code/quantJunc_TEST.sh
Untracked: code/quantJunc_removeBad.sh
Untracked: code/quantLiftedPAS.err
Untracked: code/quantLiftedPAS.out
Untracked: code/quantLiftedPAS.sh
Untracked: code/quantLiftedPASPrimary.err
Untracked: code/quantLiftedPASPrimary.out
Untracked: code/quantLiftedPASPrimary.sh
Untracked: code/quatJunc.err
Untracked: code/quatJunc.out
Untracked: code/recChimpback2Human.err
Untracked: code/recChimpback2Human.out
Untracked: code/recLiftchim2human.sh
Untracked: code/revLift.err
Untracked: code/revLift.out
Untracked: code/revLiftPAShg38to19.sh
Untracked: code/reverseLift.sh
Untracked: code/runCheckReverseLift.sh
Untracked: code/runChimpDiffIso.sh
Untracked: code/runChimpDiffIsoDF.sh
Untracked: code/runCountNucleotides.err
Untracked: code/runCountNucleotides.out
Untracked: code/runCountNucleotides.sh
Untracked: code/runCountNucleotidesPantro6.err
Untracked: code/runCountNucleotidesPantro6.out
Untracked: code/runCountNucleotides_pantro6.sh
Untracked: code/runFilterNumChroms.sh
Untracked: code/runHumanDiffIso.sh
Untracked: code/runHumanDiffIsoDF.sh
Untracked: code/runNewDom.sh
Untracked: code/runNuclearDiffIso_DF.sh
Untracked: code/runNuclearDifffIso.sh
Untracked: code/runTotalDiffIso.sh
Untracked: code/run_Chimpleafcutter_ds.err
Untracked: code/run_Chimpleafcutter_ds.out
Untracked: code/run_Chimpverifybam.err
Untracked: code/run_Chimpverifybam.out
Untracked: code/run_Humanleafcutter_dF.err
Untracked: code/run_Humanleafcutter_dF.out
Untracked: code/run_Humanleafcutter_ds.err
Untracked: code/run_Humanleafcutter_ds.out
Untracked: code/run_Nuclearleafcutter_ds.err
Untracked: code/run_Nuclearleafcutter_ds.out
Untracked: code/run_Nuclearleafcutter_dsDF.err
Untracked: code/run_Nuclearleafcutter_dsDF.out
Untracked: code/run_Totalleafcutter_ds.err
Untracked: code/run_Totalleafcutter_ds.out
Untracked: code/run_chimpverifybam.sh
Untracked: code/run_verifyBam.sh
Untracked: code/run_verifybam.err
Untracked: code/run_verifybam.out
Untracked: code/slurm-62824013.out
Untracked: code/slurm-62825841.out
Untracked: code/slurm-62826116.out
Untracked: code/slurm-64108209.out
Untracked: code/slurm-64108521.out
Untracked: code/slurm-64108557.out
Untracked: code/snakePASChimp.err
Untracked: code/snakePASChimp.out
Untracked: code/snakePAShuman.out
Untracked: code/snakemake.batch
Untracked: code/snakemakeChimp.err
Untracked: code/snakemakeChimp.out
Untracked: code/snakemakeHuman.err
Untracked: code/snakemakeHuman.out
Untracked: code/snakemakePAS.batch
Untracked: code/snakemakePASFiltChimp.err
Untracked: code/snakemakePASFiltChimp.out
Untracked: code/snakemakePASFiltHuman.err
Untracked: code/snakemakePASFiltHuman.out
Untracked: code/snakemakePAS_Human.batch
Untracked: code/snakemakePASchimp.batch
Untracked: code/snakemakePAShuman.batch
Untracked: code/snakemake_chimp.batch
Untracked: code/snakemake_human.batch
Untracked: code/snakemakefiltPAS.batch
Untracked: code/snakemakefiltPAS_chimp.sh
Untracked: code/snakemakefiltPAS_human.batch
Untracked: code/snakemakefiltPAS_human.sh
Untracked: code/spliceSite2Fasta.py
Untracked: code/submit-snakemake-chimp.sh
Untracked: code/submit-snakemake-human.sh
Untracked: code/submit-snakemakePAS-chimp.sh
Untracked: code/submit-snakemakePAS-human.sh
Untracked: code/submit-snakemakefiltPAS-chimp.sh
Untracked: code/submit-snakemakefiltPAS-human.sh
Untracked: code/subset_diffisopheno.py
Untracked: code/subset_diffisopheno_Chimp_tvN.py
Untracked: code/subset_diffisopheno_Chimp_tvN_DF.py
Untracked: code/subset_diffisopheno_Huma_tvN.py
Untracked: code/subset_diffisopheno_Huma_tvN_DF.py
Untracked: code/subset_diffisopheno_Nuclear_HvC.py
Untracked: code/subset_diffisopheno_Nuclear_HvC_DF.py
Untracked: code/subset_diffisopheno_Total_HvC.py
Untracked: code/test
Untracked: code/test.txt
Untracked: code/threeprimeOrthoFC.out
Untracked: code/threeprimeOrthoFC.sh
Untracked: code/threeprimeOrthoFCcd.err
Untracked: code/transcriptDTplotsNuclear.sh
Untracked: code/transcriptDTplotsTotal.sh
Untracked: code/verifyBam4973.sh
Untracked: code/verifyBam4973inHuman.sh
Untracked: code/verifybam4973.err
Untracked: code/verifybam4973.out
Untracked: code/verifybam4973HumanMap.err
Untracked: code/verifybam4973HumanMap.out
Untracked: code/wrap_Chimpverifybam.err
Untracked: code/wrap_Chimpverifybam.out
Untracked: code/wrap_chimpverifybam.sh
Untracked: code/wrap_verifyBam.sh
Untracked: code/wrap_verifybam.err
Untracked: code/wrap_verifybam.out
Untracked: code/writeMergecode.py
Untracked: data/._.DS_Store
Untracked: data/._HC_filenames.txt
Untracked: data/._HC_filenames.txt.sb-4426323c-IKIs0S
Untracked: data/._HC_filenames.xlsx
Untracked: data/._MapPantro6_meta.txt
Untracked: data/._MapPantro6_meta.txt.sb-a5794dd2-Cskmlm
Untracked: data/._MapPantro6_meta.xlsx
Untracked: data/._OppositeSpeciesMap.txt
Untracked: data/._OppositeSpeciesMap.txt.sb-a5794dd2-mayWJf
Untracked: data/._OppositeSpeciesMap.xlsx
Untracked: data/._RNASEQ_metadata.txt
Untracked: data/._RNASEQ_metadata.txt.sb-4426323c-TE4ns3
Untracked: data/._RNASEQ_metadata.txt.sb-51f67ae1-HXp7Gq
Untracked: data/._RNASEQ_metadata_2Removed.txt
Untracked: data/._RNASEQ_metadata_2Removed.txt.sb-4426323c-a4lBwx
Untracked: data/._RNASEQ_metadata_2Removed.xlsx
Untracked: data/._RNASEQ_metadata_stranded.txt
Untracked: data/._RNASEQ_metadata_stranded.txt.sb-a5794dd2-D659m2
Untracked: data/._RNASEQ_metadata_stranded.txt.sb-a5794dd2-ImNMoY
Untracked: data/._RNASEQ_metadata_stranded.txt.sb-e4bf31f0-ZGnGgl
Untracked: data/._RNASEQ_metadata_stranded.xlsx
Untracked: data/._TrialFiltersMeta.txt
Untracked: data/._TrialFiltersMeta.txt.sb-9845453e-R58Y0Q
Untracked: data/._metadata_HCpanel.txt
Untracked: data/._metadata_HCpanel.txt.sb-a3d92a2d-b9cYoF
Untracked: data/._metadata_HCpanel.txt.sb-a5794dd2-i594qs
Untracked: data/._metadata_HCpanel.txt.sb-f4823d1e-qihGek
Untracked: data/._metadata_HCpanel_frompantro5.xlsx
Untracked: data/._~$RNASEQ_metadata.xlsx
Untracked: data/._~$metadata_HCpanel.xlsx
Untracked: data/._.xlsx
Untracked: data/AREelements/
Untracked: data/BaseComp/
Untracked: data/CleanLiftedPeaks_FC_primary/
Untracked: data/CompapaQTLpas/
Untracked: data/DNDS/
Untracked: data/DTmatrix/
Untracked: data/DiffDomandDE_example/
Untracked: data/DiffExpression/
Untracked: data/DiffIso_Nuclear/
Untracked: data/DiffIso_Nuclear_DF/
Untracked: data/DiffIso_Total/
Untracked: data/DiffSplice/
Untracked: data/DiffSplice_liftedJunc/
Untracked: data/DiffSplice_removeBad/
Untracked: data/DomDefGreaterX/
Untracked: data/DomStructure_4/
Untracked: data/DominantPAS/
Untracked: data/DominantPAS_DF/
Untracked: data/EvalPantro5/
Untracked: data/HC_filenames.txt
Untracked: data/HC_filenames.xlsx
Untracked: data/Khan_prot/
Untracked: data/Li_eqtls/
Untracked: data/MapPantro6_meta.txt
Untracked: data/MapPantro6_meta.xlsx
Untracked: data/MapStats/
Untracked: data/NormalizedClusters/
Untracked: data/NuclearHvC/
Untracked: data/NuclearHvC_DF/
Untracked: data/OppositeSpeciesMap.txt
Untracked: data/OppositeSpeciesMap.xlsx
Untracked: data/OrthoExonBed/
Untracked: data/OverlapBenchmark/
Untracked: data/OverlappingPAS/
Untracked: data/PAS/
Untracked: data/PAS_SAF/
Untracked: data/PAS_doubleFilter/
Untracked: data/Peaks_5perc/
Untracked: data/Pheno_5perc/
Untracked: data/Pheno_5perc_DF_nuclear/
Untracked: data/Pheno_5perc_nuclear/
Untracked: data/Pheno_5perc_nuclear_old/
Untracked: data/Pheno_5perc_total/
Untracked: data/PhyloP/
Untracked: data/Pol2Chip/
Untracked: data/RNASEQ_metadata.txt
Untracked: data/RNASEQ_metadata_2Removed.txt
Untracked: data/RNASEQ_metadata_2Removed.xlsx
Untracked: data/RNASEQ_metadata_stranded.txt
Untracked: data/RNASEQ_metadata_stranded.txt.sb-e4bf31f0-ZGnGgl/
Untracked: data/RNASEQ_metadata_stranded.xlsx
Untracked: data/SignalSites/
Untracked: data/SignalSites_doublefilter/
Untracked: data/SpliceSite/
Untracked: data/TestAnnoBiasOE/
Untracked: data/TestMM2/
Untracked: data/TestMM2_AS/
Untracked: data/TestMM2_PrimaryRead/
Untracked: data/TestMM2_SeondaryRead/
Untracked: data/TestMM2_mismatch/
Untracked: data/TestMM2_quality/
Untracked: data/TestWithinMergePAS/
Untracked: data/Test_FC_methods/
Untracked: data/Threeprime2Ortho/
Untracked: data/TotalFractionPAS/
Untracked: data/TotalHvC/
Untracked: data/TrialFiltersMeta.txt
Untracked: data/TwoBadSampleAnalysis/
Untracked: data/Wang_ribo/
Untracked: data/apaQTLGenes/
Untracked: data/bioGRID/
Untracked: data/chainFiles/
Untracked: data/cleanPeaks_anno/
Untracked: data/cleanPeaks_byspecies/
Untracked: data/cleanPeaks_lifted/
Untracked: data/files4viz_nuclear/
Untracked: data/files4viz_nuclear_DF/
Untracked: data/gviz/
Untracked: data/leafviz/
Untracked: data/liftover_files/
Untracked: data/mediation/
Untracked: data/mediation_DF/
Untracked: data/metadata_HCpanel.txt
Untracked: data/metadata_HCpanel.xlsx
Untracked: data/metadata_HCpanel_extra.txt
Untracked: data/metadata_HCpanel_frompantro5.txt
Untracked: data/metadata_HCpanel_frompantro5.xlsx
Untracked: data/miRNA/
Untracked: data/multimap/
Untracked: data/orthoUTR/
Untracked: data/primaryLift/
Untracked: data/reverseLift/
Untracked: data/testQuant/
Untracked: data/~$RNASEQ_metadata.xlsx
Untracked: data/~$metadata_HCpanel.xlsx
Untracked: data/.xlsx
Untracked: output/._.DS_Store
Untracked: output/dAPAandDomEnrich.png
Untracked: output/dEandDomEnrich.png
Untracked: output/dtPlots/
Untracked: projectNotes.Rmd
Untracked: proteinModelSet.Rmd
Unstaged changes:
Modified: analysis/DiffTop2SecondDom.Rmd
Modified: analysis/ExploredAPA.Rmd
Modified: analysis/ExploredAPA_DF.Rmd
Modified: analysis/MMExpreiment.Rmd
Modified: analysis/OppositeMap.Rmd
Modified: analysis/PTM_analysis.Rmd
Modified: analysis/TotalDomStructure.Rmd
Modified: analysis/TotalVNuclearBothSpecies.Rmd
Modified: analysis/annotationInfo.Rmd
Modified: analysis/changeMisprimcut.Rmd
Modified: analysis/comp2apaQTLPAS.Rmd
Modified: analysis/correlationPhenos.Rmd
Modified: analysis/establishCutoffs.Rmd
Modified: analysis/investigatePantro5.Rmd
Modified: analysis/multiMap.Rmd
Modified: analysis/pol2.Rmd
Modified: analysis/signalsites_doublefilter.Rmd
Modified: analysis/speciesSpecific.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 | e513e9f | brimittleman | 2020-04-23 | add dot chart |
| html | 5d82297 | brimittleman | 2020-04-22 | Build site. |
| Rmd | 1e814ec | brimittleman | 2020-04-22 | change colors |
| html | 6d8725a | brimittleman | 2020-04-10 | Build site. |
| Rmd | fbabfb7 | brimittleman | 2020-04-10 | remove 18499 |
| html | c2a0778 | brimittleman | 2020-04-06 | Build site. |
| Rmd | 9676d72 | brimittleman | 2020-04-06 | updated anno |
| html | fa2369c | brimittleman | 2020-03-18 | Build site. |
| Rmd | d481350 | brimittleman | 2020-03-18 | add more upand down controll |
| html | 0bbaafe | brimittleman | 2020-03-05 | Build site. |
| Rmd | 0665dda | brimittleman | 2020-03-05 | add downstream control |
| html | 7b73ce7 | brimittleman | 2020-03-04 | Build site. |
| Rmd | ad95271 | brimittleman | 2020-03-04 | add upstream control |
In my initial exploration of dAPA PAS I saw they are enriched for negative phylop scores. I will explore this trend further here. I will see if intron location explain the differences.
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(ggpubr)Loading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithsDiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
NucReswPhy=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr","start","end"))ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")
ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", name="Signficant PAS") + labs(title="Mean PhyloP scores for tested PAS") + annotate("text",label="Wilcoxan, p=1.4e -5",x=6,y=.75)
The significant PAS have on average lower phyloP scores.
Positive scores — Measure conservation, which is slower evolution than expected, at sites that are predicted to be conserved. Negative scores — Measure acceleration, which is faster evolution than expected, at sites that are predicted to be fast-evolving.
I can look at those with negative values:
x=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
m= nrow(NucReswPhy %>% filter(phyloP<0))
n=nrow(NucReswPhy %>% filter(phyloP>=0))
k=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 559#actual:
x[1] 604#pval
phyper(x,m,n,k,lower.tail=F)[1] 0.01326693b=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
n=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhy %>% filter(phyloP<0))
N=nrow(NucReswPhy)
(b/n)/(B/N)[1] 1.079162This means these regions are more likely to be fast evolving.
Look at this by location: (is it driven by region)
NucReswPhy_meta= NucReswPhy %>% inner_join(PASMeta, by=c("chr", "start", "end", "gene"))
ggplot(NucReswPhy_meta,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2") + facet_grid(~loc)
NucReswPhy_meta_group=NucReswPhy_meta %>% group_by(loc,SigPAU2) %>% summarise(n=n(),meanPhylo=mean(phyloP))
NucReswPhy_meta_group# A tibble: 10 x 4
# Groups: loc [5]
loc SigPAU2 n meanPhylo
<chr> <chr> <int> <dbl>
1 cds No 7048 2.15
2 cds Yes 262 2.14
3 end No 3574 0.372
4 end Yes 172 0.324
5 intron No 13484 0.0622
6 intron Yes 544 0.0833
7 utr3 No 15408 1.04
8 utr3 Yes 1280 0.904
9 utr5 No 1158 0.300
10 utr5 Yes 81 0.261 Control sequence:upstream
(upstream 200)
Look at the 200 basepairs upstream of each PAS as a control.
metaStrand=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(chr, start,end, strandFix, PAS)
NucReswPhy_upstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+", start - 200, end), newEnd=ifelse(strandFix=="+", start, end +200))
NucReswPhy_upstreambed=NucReswPhy_upstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed,"../data/PhyloP/PAS_200upregions.bed",col.names = F,row.names = F,quote = F,sep="\t")python extractPhylopReg200up.py
Phylo200UpContron=read.table("../data/PhyloP/PAS_phyloP_200upstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","UpstreamControl_Phylop"))
NucReswPhyandC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS") %>% drop_na()
NucReswPhyandCsmall=NucReswPhyandC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2)
wilcox.test(NucReswPhyandC$phyloP, NucReswPhyandC$UpstreamControl_Phylop, alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandC$phyloP and NucReswPhyandC$UpstreamControl_Phylop
W = 1088300000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0Actual are greater than region upstream
ggplot(NucReswPhyandCsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c('PAS', 'Control') ) + stat_compare_means()
NucReswPhyandCsmall_noc= NucReswPhyandCsmall %>% filter(set!="UpstreamControl_Phylop")
ggplot(NucReswPhyandCsmall_noc, aes(x=SigPAU2, fill=SigPAU2, y=Phylop )) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "OrRd") + labs(title="Significant PAS",x="")+ scale_x_discrete(labels=c("Not Significant", "Signficant"))+ theme(legend.position = "none",text= element_text(size=16))
Significant are lower than not significant:
NucReswPhyandCsmall_nocYES= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="Yes")
NucReswPhyandCsmall_nocNO= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="No")
wilcox.test(NucReswPhyandCsmall_nocYES$Phylop, NucReswPhyandCsmall_nocNO$Phylop, alternative ="less")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandCsmall_nocYES$Phylop and NucReswPhyandCsmall_nocNO$Phylop
W = 46695000, p-value = 0.0733
alternative hypothesis: true location shift is less than 0Significant have lower scores.
Negative enrichment
Number of negative in each set?
neg=NucReswPhyandCsmall %>% filter(Phylop <0) %>% group_by(set, SigPAU2) %>% summarise(nNeg=n())
pos=NucReswPhyandCsmall %>% filter(Phylop >0) %>% group_by(set, SigPAU2) %>% summarise(nPos=n())
both=neg %>% inner_join(pos,by= c('set', 'SigPAU2')) %>% mutate(PropNeg=nNeg/(nNeg+nPos))
both# A tibble: 4 x 5
# Groups: set [2]
set SigPAU2 nNeg nPos PropNeg
<chr> <chr> <int> <int> <dbl>
1 phyloP No 9685 30967 0.238
2 phyloP Yes 604 1735 0.258
3 UpstreamControl_Phylop No 13229 27423 0.325
4 UpstreamControl_Phylop Yes 817 1522 0.349More negative overall in actual. Is there an enrichment for negative in the control set?
x=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
m= nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop>=0))
k=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 764#actual:
x[1] 817#pval
phyper(x,m,n,k,lower.tail=F)[1] 0.00806816b=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
N=nrow(NucReswPhyandC)
(b/n)/(B/N)[1] 1.069096Stronger enrichement in the for negative in the real results compared to contol. 1.07x in control 1.11x in actual.
Maybe I need to move the control further up.
Is this a better control? Dont want to go into an exon? What about downstream?
Control sequence (downstream 200):
NucReswPhy_downstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+", end, start-200), newEnd=ifelse(strandFix=="+", end+200, start))
NucReswPhy_downstreambed=NucReswPhy_downstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)
write.table(NucReswPhy_downstreambed,"../data/PhyloP/PAS_200downpregions.bed",col.names = F,row.names = F,quote = F,sep="\t")python extractPhylopReg200down.py
Phylo200downCont=read.table("../data/PhyloP/PAS_phyloP_200downstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","DownstreamControl_Phylop"))
NucReswPhyandbothC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS") %>% drop_na() %>% inner_join(Phylo200downCont, by="PAS") %>% drop_na()
NucReswPhyandCbothsmall=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2) %>% drop_na()
#difference in controls?
wilcox.test(NucReswPhyandbothC$DownstreamControl_Phylop, NucReswPhyandbothC$UpstreamControl_Phylop,alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandbothC$DownstreamControl_Phylop and NucReswPhyandbothC$UpstreamControl_Phylop
W = 23487000, p-value = 1
alternative hypothesis: true location shift is greater than 0levels=NucReswPhyandCbothsmall$set %>% unique()
NucReswPhyandCbothsmall$set= factor(NucReswPhyandCbothsmall$set, levels = c("UpstreamControl_Phylop", "phyloP", "DownstreamControl_Phylop"))
my_comparisons <- list( c("DownsreamControl_Pylop", "phylopP"), c("DownsreamControl_Pylop", "UpstreamControl_Phylop"), c("phylopP", "UpstreamControl_Phylop") )
ggplot(NucReswPhyandCbothsmall, aes(x=set, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Upstream Control", "PAS", "Downstream Control") ) + stat_compare_means(ref.group = "phyloP",paired = FALSE,label = "p.signif") + labs(x="", title="PAS conserved compared to surrounding regions" ) + scale_x_discrete( labels=c("Upstream Control", "PAS", "Downstream Control"))+ theme(legend.position = "none",text= element_text(size=16))
Same here. The actual region looks more conserved.
ggplot(NucReswPhyandCbothsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Downstream Control", "PAS", "Upstream Control") )
Extend
2 more blocks up and downstream to add to plot.
Extend downstream:
NucReswPhy_downstream2=NucReswPhy_downstream %>% mutate(newStart2=ifelse(strandFix=="+", newEnd, newStart-200), newEnd2=ifelse(strandFix=="+", newEnd+200, newStart))
NucReswPhy_downstream2bed=NucReswPhy_downstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)
write.table(NucReswPhy_downstream2bed,"../data/PhyloP/PAS_200downpregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")
NucReswPhy_downstream3=NucReswPhy_downstream2 %>% mutate(newStart3=ifelse(strandFix=="+", newEnd2, newStart2-200), newEnd3=ifelse(strandFix=="+", newEnd2+200, newStart2))
NucReswPhy_downstream3bed=NucReswPhy_downstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)
write.table(NucReswPhy_downstream3bed,"../data/PhyloP/PAS_200downpregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")Extend upstream:
NucReswPhy_upstream2=NucReswPhy_upstream %>% mutate(newStart2=ifelse(strandFix=="+", newStart - 200, newEnd), newEnd2=ifelse(strandFix=="+", newStart, newEnd +200))
NucReswPhy_upstreambed2=NucReswPhy_upstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed2,"../data/PhyloP/PAS_200upregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")
NucReswPhy_upstream3=NucReswPhy_upstream2 %>% mutate(newStart3=ifelse(strandFix=="+", newStart2 - 200, newEnd2), newEnd3=ifelse(strandFix=="+", newStart2, newEnd2 +200))
NucReswPhy_upstreambed3=NucReswPhy_upstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed3,"../data/PhyloP/PAS_200upregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")Run phylop for each of these:
python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions2.bed ../data/PhyloP/PAS_phyloP_200downstream2.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions3.bed ../data/PhyloP/PAS_phyloP_200downstream3.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions2.bed ../data/PhyloP/PAS_phyloP_200upstream2.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions3.bed ../data/PhyloP/PAS_phyloP_200upstream3.txt
ResUpdown=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop )
Down2=read.table("../data/PhyloP/PAS_phyloP_200downstream2.txt",col.names = c("chr", "start", "end", "PAS", "Down2"),stringsAsFactors = F) %>% select(PAS, Down2)%>% drop_na()
Down3=read.table("../data/PhyloP/PAS_phyloP_200downstream3.txt",col.names = c("chr", "start", "end", "PAS", "Down3"),stringsAsFactors = F) %>% select(PAS, Down3)%>% drop_na()
Up2=read.table("../data/PhyloP/PAS_phyloP_200upstream2.txt",col.names = c("chr", "start", "end", "PAS", "Up2"),stringsAsFactors = F) %>% select(PAS, Up2)%>% drop_na()
Up3=read.table("../data/PhyloP/PAS_phyloP_200upstream3.txt",col.names = c("chr", "start", "end", "PAS", "Up3"),stringsAsFactors = F) %>% select(PAS, Up3)%>% drop_na()
ResUpdownAll= ResUpdown %>% inner_join(Down2, by="PAS")%>% inner_join(Down3, by="PAS") %>% inner_join(Up2, by="PAS") %>% inner_join(Up3, by="PAS")
ResUpdownAll_gather= ResUpdownAll %>% gather("Set", "PhyloP", -PAS, -SigPAU2)
ResUpdownAll_gather$Set=factor(ResUpdownAll_gather$Set, levels=c("Up3", "Up2","UpstreamControl_Phylop", "phyloP","DownstreamControl_Phylop", "Down2", "Down3" ))ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + facet_grid(~SigPAU2)+ scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
Change colors:
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| 5d82297 | brimittleman | 2020-04-22 |
Color just PAS and surrounding:
ResUpdownAll_gather2= ResUpdownAll_gather %>% mutate(region=ifelse(Set=="phyloP", "Yes", "No"))
ggplot(ResUpdownAll_gather2, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| 5d82297 | brimittleman | 2020-04-22 |
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] reshape2_1.4.3 ggpubr_0.2 magrittr_1.5 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 cellranger_1.1.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[28] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[31] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 lazyeval_0.2.1
[43] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[46] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[49] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[52] R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[55] compiler_3.5.1