Last updated: 2020-01-26

Checks: 7 0

Knit directory: Comparative_APA/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.5.0). 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/mediation_prot/
    Ignored:    data/metadata_HCpanel.txt.sb-a5794dd2-i594qs/

Untracked files:
    Untracked:  ._.DS_Store
    Untracked:  Chimp/
    Untracked:  Human/
    Untracked:  analysis/CrossChimpThreePrime.Rmd
    Untracked:  analysis/DiffTransProtvsExpression.Rmd
    Untracked:  analysis/assessReadQual.Rmd
    Untracked:  analysis/diffExpressionPantro6.Rmd
    Untracked:  code/._ClassifyLeafviz.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/._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/._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/._ReverseLiftFilter.R
    Untracked:  code/._RunFixLeafCluster.sh
    Untracked:  code/._RunNegMCMediation.sh
    Untracked:  code/._RunNegMCMediationDF.sh
    Untracked:  code/._RunPosMCMediationDF.err
    Untracked:  code/._RunPosMCMediationDF.sh
    Untracked:  code/._Snakefile
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilePASfilt
    Untracked:  code/._SortIndexBadSamples.sh
    Untracked:  code/._bed215upbed.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/._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/._filter5percPAS.py
    Untracked:  code/._filterNumChroms.py
    Untracked:  code/._filterPASforMP.py
    Untracked:  code/._filterPostLift.py
    Untracked:  code/._fixExonFC.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/._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/._mergedBam2BW.sh
    Untracked:  code/._nameClusters.py
    Untracked:  code/._negativeMediation_montecarlo.R
    Untracked:  code/._negativeMediation_montecarloDF.R
    Untracked:  code/._numMultimap.py
    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/._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/._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/ClassifyLeafviz.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/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/FilterReverseLift.err
    Untracked:  code/FilterReverseLift.out
    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/HchromOrder.err
    Untracked:  code/HchromOrder.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/MergeClusters.err
    Untracked:  code/MergeClusters.out
    Untracked:  code/MergeClusters.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/QuantMergeClusters
    Untracked:  code/QuantMergeClusters.err
    Untracked:  code/QuantMergeClusters.out
    Untracked:  code/QuantMergedClusters.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/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/Snakefile
    Untracked:  code/SnakefilePAS
    Untracked:  code/SnakefilePASfilt
    Untracked:  code/SortIndexBadSamples.err
    Untracked:  code/SortIndexBadSamples.out
    Untracked:  code/SortIndexBadSamples.sh
    Untracked:  code/TotalTranscriptDTplot.err
    Untracked:  code/TotalTranscriptDTplot.out
    Untracked:  code/Upstream10Bases_general.py
    Untracked:  code/apaQTLsnake.err
    Untracked:  code/apaQTLsnake.out
    Untracked:  code/apaQTLsnakePAS.err
    Untracked:  code/apaQTLsnakePAS.out
    Untracked:  code/apaQTLsnakePAShuman.err
    Untracked:  code/bam2junc.err
    Untracked:  code/bam2junc.out
    Untracked:  code/bam2junc_remove.err
    Untracked:  code/bam2junc_remove.out
    Untracked:  code/bed215upbed.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/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/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/filterSAFforMP_gen.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/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/getRNAseqMapStats.sh
    Untracked:  code/hg19MapStats.err
    Untracked:  code/hg19MapStats.out
    Untracked:  code/hg19MapStats.sh
    Untracked:  code/humanChromorder.sh
    Untracked:  code/humanFiles
    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/merge_leafcutter_clusters_redo.py
    Untracked:  code/mergeandsort_ChimpinHuman.err
    Untracked:  code/mergeandsort_ChimpinHuman.out
    Untracked:  code/mergedBam2BW.sh
    Untracked:  code/mergedbam2bw.err
    Untracked:  code/mergedbam2bw.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/overlapPAS.err
    Untracked:  code/overlapPAS.out
    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/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/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/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_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/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.sh
    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_Huma_tvN.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/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/._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.xlsx
    Untracked:  data/._metadata_HCpanel_frompantro5.xlsx
    Untracked:  data/._~$RNASEQ_metadata.xlsx
    Untracked:  data/._~$metadata_HCpanel.xlsx
    Untracked:  data/._.xlsx
    Untracked:  data/CompapaQTLpas/
    Untracked:  data/DNDS/
    Untracked:  data/DTmatrix/
    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/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/OverlapBenchmark/
    Untracked:  data/PAS/
    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/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/Threeprime2Ortho/
    Untracked:  data/TotalHvC/
    Untracked:  data/TwoBadSampleAnalysis/
    Untracked:  data/Wang_ribo/
    Untracked:  data/apaQTLGenes/
    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/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_frompantro5.txt
    Untracked:  data/metadata_HCpanel_frompantro5.xlsx
    Untracked:  data/primaryLift/
    Untracked:  data/reverseLift/
    Untracked:  data/~$RNASEQ_metadata.xlsx
    Untracked:  data/~$metadata_HCpanel.xlsx
    Untracked:  data/.xlsx
    Untracked:  output/dtPlots/
    Untracked:  projectNotes.Rmd

Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/speciesSpecific_DF.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 10d6e05 brimittleman 2020-01-26 add gene level phyloP
html 26e6362 brimittleman 2020-01-24 Build site.
Rmd d867ef6 brimittleman 2020-01-24 by loc
html 5910b06 brimittleman 2020-01-24 Build site.
Rmd ea17340 brimittleman 2020-01-24 add phylo/dnds/go
html 5800231 brimittleman 2020-01-22 Build site.
Rmd 117fd63 brimittleman 2020-01-22 redo differential analysis with double filt

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

    extract
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

I want to look more at the genes we found with dAPA.

Question 1:

Do genes with differential APA have different numbers of PAS in each species?

DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_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"))

Number of PAS in each species:

PAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", stringsAsFactors = F, header = T)
PAS_sm=PAS %>% dplyr::select(gene, Chimp, Human) 
PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.1) %>% group_by(species, gene) %>% summarise(nPAS=n())

Filter these by those with dAPA:

PAS_m_dAPA= PAS_m %>% mutate(dAPA=ifelse(gene %in% DiffUsagePAS$gene, "Y", "N"))
ggplot(PAS_m_dAPA,aes(by=dAPA, y=nPAS,x=species, fill=dAPA)) + geom_boxplot()  + stat_compare_means(method = "t.test") + scale_fill_brewer(palette = "Dark2") + labs(y="Number of PAS detected at 10% usage", title="Number of PAS detected by gene with differential usage") 

Version Author Date
5800231 brimittleman 2020-01-22

Question 2: Where are the differentially used PAS?

ggplot(DiffUsagePAS,aes(x=loc, fill=loc)) + geom_bar(stat="count") 

Version Author Date
5800231 brimittleman 2020-01-22

Seperate by location:

#negative deltaPAU is used more in human 
DiffUsagePAS_dir= DiffUsagePAS %>% mutate(direction=ifelse(deltaPAU >=0, "Chimp", "Human"))

ggplot(DiffUsagePAS_dir,aes(x=loc, fill=loc)) + geom_bar(stat="count")  + facet_grid(~direction)

Version Author Date
5800231 brimittleman 2020-01-22

This is opposite of the results using just the dominant PAS. I probably shouldn’t put too much into that.

Question 3: Does locaiton of the PAS effect the absolute value of the effect size

ggplot(DiffUsagePAS_dir,aes(x=loc, y=abs(deltaPAU), fill=loc)) + geom_violin() 

Version Author Date
5800231 brimittleman 2020-01-22

Explore conservation:

https://www.ultraconserved.org

https://useast.ensembl.org/info/genome/compara/conservation_and_constrained.html

phylo p from genomebrowser

mkdir ../data/PhyloP
mkdir ../data/DNDS

PhyloP: Column #1 contains a one-based position coordinate. Column #2 contains a score showing the posterior probability that the phylogenetic hidden Markov model (HMM) of phastCons is in its most conserved state at that base position.

I want to get the average score for each of the tested PAS. I can use pybigwig.

python extractPhyloReg.py
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"))

40756 have results of the 40776 Plot:

ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2")

Version Author Date
5910b06 brimittleman 2020-01-24
ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2")

Version Author Date
5910b06 brimittleman 2020-01-24

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] 707
#actual:
x
[1] 788
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.0001570509

This 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)

Version Author Date
26e6362 brimittleman 2020-01-24
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       7141    2.16  
 2 cds    Yes       333    2.16  
 3 end    No       3564    0.450 
 4 end    Yes       247    0.403 
 5 intron No      10478    0.0630
 6 intron Yes       737    0.0702
 7 utr3   No      15351    1.04  
 8 utr3   Yes      1659    0.933 
 9 utr5   No       1151    0.300 
10 utr5   Yes        95    0.230 

Compare this to the genes that are expressed for this I will need to make a bedfile with the genes. I will pull them in as well as the gene annotation:

DAPAGeneSig=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", stringsAsFactors = F, header = T) 

DAPAGene=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", stringsAsFactors = F, header = T) %>% dplyr::select(gene) %>% unique() %>% mutate(Sig=ifelse(gene %in% DAPAGeneSig$gene,"Yes","No"))

To be safe ill use the longest transcripts from table browser refseq.

This will be easiest in a python dictionary.

python parseHg38.py
sort -k1,1 -k2,2n ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed.bed > ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed_sort.bed
genes=read.table("../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed_sort.bed", col.names = c("chr", "start", "end","name","score","strand"),stringsAsFactors = F) %>% filter(name %in% DAPAGene$gene ) %>% rename("gene"=name)
genesWithSig= genes %>% inner_join(DAPAGene, by="gene")
write.table(genes, "../data/PhyloP/NuclearSigGenes.bed", col.names = F, row.names = F, quote=F, sep="\t")

Get the mean phyloP scores.

 python extractPhyloRegGene.py 
phyloresG=read.table("../data/PhyloP/PAS_phyloP_genes.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
GenesPhy=genesWithSig %>% inner_join(phyloresG, by=c("chr","start","end"))

I lose 2237 from NAs in the values.

ggplot(GenesPhy,aes(x=phyloP, by=Sig, fill=Sig)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2")

ggplot(GenesPhy,aes(y=phyloP, x=Sig,fill=Sig)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2")

These are also genes with a shift. See if more likely to have - value.

x=nrow(GenesPhy %>% filter(Sig=="Yes", phyloP<0))
m= nrow(GenesPhy %>% filter(phyloP<0))
n=nrow(GenesPhy %>% filter(phyloP>=0))
k=nrow(GenesPhy %>% filter(Sig=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 202
#actual:
x
[1] 246
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 7.212381e-05

Enriched for genes with - scores.

DN (non synonymous) /DS (synonymous): from ensamble site - ratio of substitution rate (quick and dirty way to look at evo), ration >1 usually evidence for positive selection. values are in ../data/DNDS/HumanChimp_DNDS.csv

Remove NA values

DNDS= read.csv("../data/DNDS/HumanChimp_DNDS.csv", header = T,stringsAsFactors = F) %>% drop_na() %>% group_by(Gene.name) %>% slice(1) %>% ungroup() %>% mutate(DNDSratio= dN.with.Chimpanzee/dS.with.Chimpanzee) %>% dplyr::select(Gene.name, dN.with.Chimpanzee,dS.with.Chimpanzee,DNDSratio) %>% rename("gene"=Gene.name)

Join with all results then subset based on significance:

I will get all genes,

NucResGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T)
NucResAll=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% dplyr::select(gene) %>% unique() %>% mutate(SigPASinGene=ifelse(gene %in% NucResGenes$gene, "yes", "no")) 

NucResDNDS= NucResAll %>% inner_join(DNDS,by="gene") 

We do not have information for 1236 of the genes. I will assess results on the 7308 with data. There are also genes with ratio problems due to zero in the ds column. If it is infinity, i can make it 1 for now because there are only fixed non syn mutations fixing. If both are 0 I will make it 0.

NucResDNDS_fix=NucResDNDS %>% mutate(DNDSratio = replace_na(DNDSratio,0))

NucResDNDS_fix[NucResDNDS_fix == Inf] <- 1

summary(NucResDNDS_fix$DNDSratio)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
  0.00000   0.03571   0.19797   0.34906   0.45455 147.00000 
NucResDNDS_fix %>% group_by(SigPASinGene) %>% summarise(n=n())
# A tibble: 2 x 2
  SigPASinGene     n
  <chr>        <int>
1 no            5481
2 yes           1827

Plot this.

ggplot(NucResDNDS_fix,aes(y=log10(DNDSratio+1), x=SigPASinGene, fill=SigPASinGene))+ geom_boxplot() + stat_compare_means( label.y.npc = "middle") + scale_fill_brewer(palette = "Dark2") + labs(x="dAPA in Nuclear") + annotate("text", label="Yes=1827 \n No=5481", y=1.8,x=2)

I can ask if they are more likely to be above 1. I can do this with a hypergeo.

x=nrow(NucResDNDS_fix %>% filter(SigPASinGene=="yes", DNDSratio>=1))
m= nrow(NucResDNDS_fix %>% filter(DNDSratio>=1))
n=nrow(NucResDNDS_fix %>% filter(DNDSratio<1))
k=nrow(NucResDNDS_fix %>% filter(SigPASinGene=="yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 115
#actual:
x
[1] 115
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.6666078

No enrichment for positive selected genes.

Gene ontology: Need a ranked list of genes. I can do this for the differential apa genes by pvalue.
http://cbl-gorilla.cs.technion.ac.il

NucRes=read.table("../data/DiffIso_Nuclear/SignifianceEitherPAS_2_Nuclear.txt",header = T,stringsAsFactors = F) %>% arrange(p.adjust) %>% dplyr::select(gene) %>% unique()


write.table(NucRes,"../data/DiffIso_Nuclear/SignifianceGenes_orderPval.txt",col.names = F, row.names = F, quote = F)

Use gorilla:

Top results:

RNA binding

translation factor activity, RNA binding

protein-containing complex

eukaryotic translation initiation factor

cellular protein-containing complex assembly

intracellular transport

establishment of localization in cell   

protein targeting to membrane

nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 
     
translational initiation     

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_1.5.0    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