Last updated: 2020-01-21

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.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190902)

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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

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

Repository version: 2e66af9

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/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/._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/._PASsequences.sh
    Untracked:  code/._PASsequences_DF.sh
    Untracked:  code/._PlotNuclearUsagebySpecies.R
    Untracked:  code/._QuantMergedClusters.sh
    Untracked:  code/._ReverseLiftFilter.R
    Untracked:  code/._RunFixLeafCluster.sh
    Untracked:  code/._RunNegMCMediation.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/._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/._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/._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/._numMultimap.py
    Untracked:  code/._overlapapaQTLPAS.sh
    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/._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_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/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_sequence.err
    Untracked:  code/PAS_sequence.out
    Untracked:  code/PAS_sequenceDF.err
    Untracked:  code/PAS_sequenceDF.out
    Untracked:  code/PASsequences.sh
    Untracked:  code/PASsequences_DF.sh
    Untracked:  code/PlotNuclearUsagebySpecies.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/RunNegMCMediationr.out
    Untracked:  code/RunPosMCMediation.err
    Untracked:  code/RunPosMCMediation.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/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/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/makeNuclearPlots.err
    Untracked:  code/makeNuclearPlots.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/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/peak2PAS.py
    Untracked:  code/pheno2countonly.R
    Untracked:  code/postiveMediation_montecarlo.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/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_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_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/DTmatrix/
    Untracked:  data/DiffExpression/
    Untracked:  data/DiffIso_Nuclear/
    Untracked:  data/DiffIso_Total/
    Untracked:  data/DiffSplice/
    Untracked:  data/DiffSplice_liftedJunc/
    Untracked:  data/DiffSplice_removeBad/
    Untracked:  data/DominantPAS/
    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/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_nuclear/
    Untracked:  data/Pheno_5perc_nuclear_old/
    Untracked:  data/Pheno_5perc_total/
    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/leafviz/
    Untracked:  data/liftover_files/
    Untracked:  data/mediation/
    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/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

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 2e66af9 brimittleman 2020-01-21 add ss and PAS num DF

In this analysis I will look at the signal site distributions for the human and chimp PAS I have called.

library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

I am looking at 200 base pair regions for each pas. I will look for the sequence in these for now and then refine the search.

I can use bedtools nuc on both to get the sequences for the bed files in ../data/PAS.

mkdir ../data/SignalSites_doublefilter
sbatch PASsequences_DF.sh

The way I did this it flipped the - strand and assayed the correct strand sequence. I will still have to make everything upper case.

Before I use python to find the occurances. I will look at the results because I gave the AATAAA pattern to the nuc program to assay.

First i have to remove the # in each file

humanRawout=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_nuc.txt", stringsAsFactors = F, header = T) %>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))
ChimpRawout=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_nuc.txt", stringsAsFactors = F, header = T)%>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))

Histogram for the results:

ggplot(humanRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..)) +labs(title="Distribution of AATAAA pattern Human")

ggplot(ChimpRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..))+labs(title="Distribution of AATAAA pattern Chimps")

See if yes no segragates with usage:

ggplot(humanRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

ggplot(ChimpRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

Look at location data and bring this in.

Loc=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% rename("X4_usercol"=PAS) %>% dplyr::select(X4_usercol,loc)

ChimpRawout_withloc=ChimpRawout %>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
humanRawout_withloc=humanRawout%>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
ggplot(humanRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

ggplot(ChimpRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

I can run the nuc command again for the other doninant signal site I found in the apaQTL analysis (ATTAAA), I can join the results.

sbatch PAS_ATTAAA_df.sh

remove #

human_ATTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))

chimp_ATTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))


human_both=human_ATTAAA %>% inner_join(humanRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))

chimp_both=chimp_ATTAAA %>% inner_join(ChimpRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))
ggplot(human_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of  ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

ggplot(human_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Plot percentage either by loc:

human_both_loc= human_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Human=count/nLoc) %>%ungroup() %>%  dplyr::select(loc, anySS,Human)

chimp_both_loc= chimp_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Chimp=count/nLoc)%>% ungroup() %>% dplyr::select(loc, anySS,Chimp)

bothSpeciesLoc=chimp_both_loc %>% inner_join(human_both_loc,by=c("loc", "anySS")) %>% gather(key="species", value="propSS", -loc, -anySS) %>% filter(anySS=="yes")


ggplot(bothSpeciesLoc, aes(x=loc, fill=species,y=propSS)) + geom_bar(stat="identity",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Presence of top 2 signal sites by location", x="Proportion with signal site", x="location")

Write out information about SS so i can use it for other anaylsis.

human_write=human_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(human_write, "../data/SignalSites_doublefilter/HumanPresenceofSS_DF.txt", col.names = T, row.names = F, quote = F)

chimp_write=chimp_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(chimp_write,"../data/SignalSites_doublefilter/ChimpPresenceofSS_DF.txt", col.names = T, row.names = F, quote = F)

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    tidyverse_1.2.1
 [9] workflowr_1.5.0 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

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         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1