Last updated: 2019-05-28

Checks: 6 0

Knit directory: apaQTL/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.3.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

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

Environment: empty

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

Seed: set.seed(20190411)

The command set.seed(20190411) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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

Cache: none

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

Repository version: c04929f

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:    output/.DS_Store

Untracked files:
    Untracked:  .Rprofile
    Untracked:  ._.DS_Store
    Untracked:  .gitignore
    Untracked:  _workflowr.yml
    Untracked:  analysis/._PASdescriptiveplots.Rmd
    Untracked:  analysis/._cuttoffPercUsage.Rmd
    Untracked:  analysis/cuttoffPercUsage.Rmd
    Untracked:  analysis/nascentRNA.Rmd
    Untracked:  apaQTL.Rproj
    Untracked:  code/._ApaQTL_nominalNonnorm.sh
    Untracked:  code/._BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/._FC_UTR.sh
    Untracked:  code/._FC_newPeaks_olddata.sh
    Untracked:  code/._LC_samplegroups.py
    Untracked:  code/._NetSeq_fourthintronDT.sh
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilefiltPAS
    Untracked:  code/._TESplots100bp.sh
    Untracked:  code/._TESplots150bp.sh
    Untracked:  code/._TESplots200bp.sh
    Untracked:  code/._Untitled
    Untracked:  code/._ZipandTabPheno.sh
    Untracked:  code/._aAPAqtl_nominal39ind.sh
    Untracked:  code/._apaQTLCorrectPvalMakeQQ.R
    Untracked:  code/._apaQTL_Nominal.sh
    Untracked:  code/._apaQTL_permuted.sh
    Untracked:  code/._assignNucIntonpeak2intronlocs.sh
    Untracked:  code/._assignTotIntronpeak2intronlocs.sh
    Untracked:  code/._bam2BW_5primemost.sh
    Untracked:  code/._bed2saf.py
    Untracked:  code/._bothFracDTplot1stintron.sh
    Untracked:  code/._bothFracDTplot4thintron.sh
    Untracked:  code/._bothFrac_FC.sh
    Untracked:  code/._callPeaksYL.py
    Untracked:  code/._chooseAnno2SAF.py
    Untracked:  code/._chooseSignalSite
    Untracked:  code/._chooseSignalSite.py
    Untracked:  code/._cluster.json
    Untracked:  code/._clusterPAS.json
    Untracked:  code/._clusterfiltPAS.json
    Untracked:  code/._codingdms2bed.py
    Untracked:  code/._config.yaml
    Untracked:  code/._config2.yaml
    Untracked:  code/._configOLD.yaml
    Untracked:  code/._convertNumeric.py
    Untracked:  code/._dag.pdf
    Untracked:  code/._encodeRNADTplots.sh
    Untracked:  code/._extractGenotypes.py
    Untracked:  code/._fc2leafphen.py
    Untracked:  code/._filter5perc.R
    Untracked:  code/._filter5percPheno.py
    Untracked:  code/._filterpeaks.py
    Untracked:  code/._finalPASbed2SAF.py
    Untracked:  code/._fix4su304corr.py
    Untracked:  code/._fix4su604corr.py
    Untracked:  code/._fix4sukalisto.py
    Untracked:  code/._fixFChead.py
    Untracked:  code/._fixFChead_bothfrac.py
    Untracked:  code/._fixH3k12ac.py
    Untracked:  code/._fixRNAhead4corr.py
    Untracked:  code/._fixRNAkalisto.py
    Untracked:  code/._fixgroupedtranscript.py
    Untracked:  code/._fixhead_netseqfc.py
    Untracked:  code/._grouptranscripts.py
    Untracked:  code/._make5percPeakbed.py
    Untracked:  code/._makeFileID.py
    Untracked:  code/._makePheno.py
    Untracked:  code/._makeSAFbothfrac5perc.py
    Untracked:  code/._makegencondeTSSfile.py
    Untracked:  code/._mergeAllBam.sh
    Untracked:  code/._mergeBW_norm.sh
    Untracked:  code/._mergeByFracBam.sh
    Untracked:  code/._mergePeaks.sh
    Untracked:  code/._mnase1stintron.sh
    Untracked:  code/._mnaseDT_fourthintron.sh
    Untracked:  code/._namePeaks.py
    Untracked:  code/._netseqDTplot1stIntron.sh
    Untracked:  code/._netseqFC.sh
    Untracked:  code/._peak2PAS.py
    Untracked:  code/._peakFC.sh
    Untracked:  code/._pheno2countonly.R
    Untracked:  code/._qtlsPvalOppFrac.py
    Untracked:  code/._quantassign2parsedpeak.py
    Untracked:  code/._removeloc_pheno.py
    Untracked:  code/._run_leafcutterDiffIso.sh
    Untracked:  code/._run_sepUsagephen.sh
    Untracked:  code/._selectNominalPvalues.py
    Untracked:  code/._sepUsagePhen.py
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._submit-snakemakePAS.sh
    Untracked:  code/._submit-snakemakefiltPAS.sh
    Untracked:  code/._subset_diffisopheno.py
    Untracked:  code/._subtrachfiveprimeUTR.sh
    Untracked:  code/._subtractExons.sh
    Untracked:  code/._subtractfiveprimeUTR.sh
    Untracked:  code/._utrdms2saf.py
    Untracked:  code/.snakemake/
    Untracked:  code/APAqtl_nominal.err
    Untracked:  code/APAqtl_nominal.out
    Untracked:  code/APAqtl_nominal_39.err
    Untracked:  code/APAqtl_nominal_39.out
    Untracked:  code/APAqtl_nominal_nonNorm.err
    Untracked:  code/APAqtl_nominal_nonNorm.out
    Untracked:  code/APAqtl_permuted.err
    Untracked:  code/APAqtl_permuted.out
    Untracked:  code/ApaQTL_nominalNonnorm.sh
    Untracked:  code/BothFracDTPlot1stintron.err
    Untracked:  code/BothFracDTPlot1stintron.out
    Untracked:  code/BothFracDTPlot4stintron.err
    Untracked:  code/BothFracDTPlot4stintron.out
    Untracked:  code/BothFracDTPlotGeneRegions.err
    Untracked:  code/BothFracDTPlotGeneRegions.out
    Untracked:  code/BothFracDTPlotGeneRegions_norm.err
    Untracked:  code/BothFracDTPlotGeneRegions_norm.out
    Untracked:  code/BothFracDTPlotGeneRegions_normalized.sh
    Untracked:  code/DistPAS2Sig.py
    Untracked:  code/EncodeRNADTPlotGeneRegions.err
    Untracked:  code/EncodeRNADTPlotGeneRegions.out
    Untracked:  code/FC_UTR.err
    Untracked:  code/FC_UTR.out
    Untracked:  code/FC_UTR.sh
    Untracked:  code/FC_newPAS_olddata.err
    Untracked:  code/FC_newPAS_olddata.out
    Untracked:  code/FC_newPeaks_olddata.sh
    Untracked:  code/LC_samplegroups.py
    Untracked:  code/NetSeq_fourthintronDT.sh
    Untracked:  code/README.md
    Untracked:  code/Rplots.pdf
    Untracked:  code/TESplots100bp.err
    Untracked:  code/TESplots100bp.out
    Untracked:  code/TESplots100bp.sh
    Untracked:  code/TESplots150bp.err
    Untracked:  code/TESplots150bp.out
    Untracked:  code/TESplots150bp.sh
    Untracked:  code/TESplots200bp.err
    Untracked:  code/TESplots200bp.out
    Untracked:  code/TESplots200bp.sh
    Untracked:  code/Untitled
    Untracked:  code/Upstream100Bases_general.py
    Untracked:  code/ZipandTabPheno.sh
    Untracked:  code/aAPAqtl_nominal39ind.sh
    Untracked:  code/apaQTLCorrectPvalMakeQQ_4pc.R
    Untracked:  code/apaQTL_Nominal_4pc.sh
    Untracked:  code/apaQTL_permuted.4pc.sh
    Untracked:  code/apaqtlfacetboxplots.R
    Untracked:  code/assignNucIntonpeak2intronlocs.sh
    Untracked:  code/assignPeak2Intronicregion.err
    Untracked:  code/assignPeak2Intronicregion.out
    Untracked:  code/assignTotIntronpeak2intronlocs.sh
    Untracked:  code/assigntotPeak2Intronicregion.err
    Untracked:  code/assigntotPeak2Intronicregion.out
    Untracked:  code/bam2BW_5primemost.sh
    Untracked:  code/bam2bw.err
    Untracked:  code/bam2bw.out
    Untracked:  code/bam2bw_5primemost.err
    Untracked:  code/bam2bw_5primemost.out
    Untracked:  code/bothFracDTplot1stintron.sh
    Untracked:  code/bothFracDTplot4thintron.sh
    Untracked:  code/bothFrac_FC.err
    Untracked:  code/bothFrac_FC.out
    Untracked:  code/bothFrac_FC.sh
    Untracked:  code/codingdms2bed.py
    Untracked:  code/dag.pdf
    Untracked:  code/dagPAS.pdf
    Untracked:  code/dagfiltPAS.pdf
    Untracked:  code/encodeRNADTplots.sh
    Untracked:  code/extractGenotypes.py
    Untracked:  code/fc2leafphen.py
    Untracked:  code/finalPASbed2SAF.py
    Untracked:  code/findbuginpeaks.R
    Untracked:  code/fix4su304corr.py
    Untracked:  code/fix4su604corr.py
    Untracked:  code/fix4sukalisto.py
    Untracked:  code/fixFChead_bothfrac.py
    Untracked:  code/fixFChead_summary.py
    Untracked:  code/fixH3k12ac.py
    Untracked:  code/fixRNAhead4corr.py
    Untracked:  code/fixRNAkalisto.py
    Untracked:  code/fixgroupedtranscript.py
    Untracked:  code/fixhead_netseqfc.py
    Untracked:  code/get100upPAS.py
    Untracked:  code/getSeq100up.sh
    Untracked:  code/getseq100up.err
    Untracked:  code/getseq100up.out
    Untracked:  code/grouptranscripts.err
    Untracked:  code/grouptranscripts.out
    Untracked:  code/grouptranscripts.py
    Untracked:  code/log/
    Untracked:  code/makeSAFbothfrac5perc.py
    Untracked:  code/makegencondeTSSfile.py
    Untracked:  code/mergeBW_norm.sh
    Untracked:  code/mergeBWnorm.err
    Untracked:  code/mergeBWnorm.out
    Untracked:  code/mnase1stintron.sh
    Untracked:  code/mnaseDTPlot1stintron.err
    Untracked:  code/mnaseDTPlot1stintron.out
    Untracked:  code/mnaseDTPlot4thintron.err
    Untracked:  code/mnaseDTPlot4thintron.out
    Untracked:  code/mnaseDT_fourthintron.sh
    Untracked:  code/netDTPlot4thintron.out
    Untracked:  code/netseqDTplot1stIntron.sh
    Untracked:  code/netseqFC.err
    Untracked:  code/netseqFC.out
    Untracked:  code/netseqFC.sh
    Untracked:  code/neyDTPlot4thintron.err
    Untracked:  code/qtlFacetBoxplots.err
    Untracked:  code/qtlFacetBoxplots.out
    Untracked:  code/qtlsPvalOppFrac.py
    Untracked:  code/removeloc_pheno.py
    Untracked:  code/run_DistPAS2Sig.err
    Untracked:  code/run_DistPAS2Sig.out
    Untracked:  code/run_distPAS2Sig.sh
    Untracked:  code/run_leafcutterDiffIso.sh
    Untracked:  code/run_leafcutter_ds.err
    Untracked:  code/run_leafcutter_ds.out
    Untracked:  code/run_qtlFacetBoxplots.sh
    Untracked:  code/run_sepUsagephen.sh
    Untracked:  code/run_sepusage.err
    Untracked:  code/run_sepusage.out
    Untracked:  code/selectNominalPvalues.py
    Untracked:  code/sepUsagePhen.py
    Untracked:  code/snakePASlog.out
    Untracked:  code/snakefiltPASlog.out
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/subtract5UTR.err
    Untracked:  code/subtract5UTR.out
    Untracked:  code/subtractExons.err
    Untracked:  code/subtractExons.out
    Untracked:  code/subtractExons.sh
    Untracked:  code/subtractfiveprimeUTR.sh
    Untracked:  code/transcriptdm2bed.py
    Untracked:  code/utrdms2saf.py
    Untracked:  code/zipandtabPhen.err
    Untracked:  code/zipandtabPhen.out
    Untracked:  data/CompareOldandNew/
    Untracked:  data/DTmatrix/
    Untracked:  data/DiffIso/
    Untracked:  data/EncodeRNA/
    Untracked:  data/ExampleQTLPlots/
    Untracked:  data/GeuvadisRNA/
    Untracked:  data/PAS/
    Untracked:  data/QTLGenotypes/
    Untracked:  data/QTLoverlap/
    Untracked:  data/QTLoverlap_nonNorm/
    Untracked:  data/README.md
    Untracked:  data/RNAseq/
    Untracked:  data/Reads2UTR/
    Untracked:  data/SignalSiteFiles/
    Untracked:  data/ThirtyNineIndQtl_nominal/
    Untracked:  data/apaQTLNominal/
    Untracked:  data/apaQTLNominal_4pc/
    Untracked:  data/apaQTLPermuted/
    Untracked:  data/apaQTLPermuted_4pc/
    Untracked:  data/apaQTLs/
    Untracked:  data/assignedPeaks/
    Untracked:  data/bam/
    Untracked:  data/bam_clean/
    Untracked:  data/bam_waspfilt/
    Untracked:  data/bed_10up/
    Untracked:  data/bed_clean/
    Untracked:  data/bed_clean_sort/
    Untracked:  data/bed_waspfilter/
    Untracked:  data/bedsort_waspfilter/
    Untracked:  data/bothFrac_FC/
    Untracked:  data/bw_norm/
    Untracked:  data/exampleQTLs/
    Untracked:  data/fastq/
    Untracked:  data/filterPeaks/
    Untracked:  data/fourSU/
    Untracked:  data/h3k27ac/
    Untracked:  data/highdiffsiggenes.txt
    Untracked:  data/inclusivePeaks/
    Untracked:  data/inclusivePeaks_FC/
    Untracked:  data/intron_analysis/
    Untracked:  data/mergedBG/
    Untracked:  data/mergedBW_byfrac/
    Untracked:  data/mergedBW_norm/
    Untracked:  data/mergedBam/
    Untracked:  data/mergedbyFracBam/
    Untracked:  data/netseq/
    Untracked:  data/nonNorm_pheno/
    Untracked:  data/nuc_10up/
    Untracked:  data/nuc_10upclean/
    Untracked:  data/peakCoverage/
    Untracked:  data/peaks_5perc/
    Untracked:  data/phenotype/
    Untracked:  data/phenotype_5perc/
    Untracked:  data/sigDiffGenes.txt
    Untracked:  data/sort/
    Untracked:  data/sort_clean/
    Untracked:  data/sort_waspfilter/
    Untracked:  nohup.out
    Untracked:  output/._.DS_Store
    Untracked:  output/._meanCorrelationPhenotypes.svg
    Untracked:  output/dtPlots/
    Untracked:  output/fastqc/
    Untracked:  output/meanCorrelationPhenotypes.svg

Unstaged changes:
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/config.yaml
    Deleted:    code/test.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd c04929f brimittleman 2019-05-28 add example code
html f4a2106 brimittleman 2019-05-28 Build site.
Rmd f10e64d brimittleman 2019-05-28 add plot grid
html 64a7d5d brimittleman 2019-05-28 Build site.
Rmd f5260bd brimittleman 2019-05-28 add results
html 28c8ca3 brimittleman 2019-05-24 Build site.
Rmd 3f63045 brimittleman 2019-05-24 add code to prepare non norm qtl

In order to compare effect sizes for the QTLs I have previously identified in an interpretable manner, I need to run the linear model with the non normalized usage. To do this I will separate the the usage (with annotation) files by chromosome and run fastqtl on these files.

library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(cowplot)

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

    ggsave

Prepare files

countsnum= APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric, APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric

id file= APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz, APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz

totAnno= read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz", stringsAsFactors = F, header = T) %>% separate(chrom, into=c("Chrchrom", "Start", "End", "ID"),sep=":") %>% mutate(Chr=str_sub(Chrchrom, 4, str_length(Chrchrom)))
                                                                                                                                                                                                                
colnamesTot= colnames(totAnno)[5:58]
totUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric", stringsAsFactors = F, header = F, col.names = colnamesTot) 

totUsageAnno=as.data.frame(cbind(Chr=totAnno$Chr, start=totAnno$Start, end=totAnno$End, ID=totAnno$ID, totUsage ))

write.table(totUsageAnno,file="../data/nonNorm_pheno/TotalUsageAllChrom.txt", col.names = T, row.names = F, quote = F, sep="\t" )
nucAnno= read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz", stringsAsFactors = F, header = T)%>% separate(chrom, into=c("Chrchrom", "Start", "End", "ID"),sep=":") %>% mutate(Chr=str_sub(Chrchrom, 4, str_length(Chrchrom)))
colnamesNuc= colnames(nucAnno)[5:58]
nucUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric", stringsAsFactors = F, header = F, col.names = colnamesNuc) 


nucUsageAnno=as.data.frame(cbind(Chr=nucAnno$Chr, start=nucAnno$Start, end=nucAnno$End, ID=nucAnno$ID, nucUsage ))

write.table(nucUsageAnno,file="../data/nonNorm_pheno/NuclearUsageAllChrom.txt", col.names = T, row.names = F, quote = F, sep="\t" )

Run QTL scripts

I will create a python script to seperate the file into each chromosome for running fastQTL.

sbatch run_sepUsagephen.sh
sbatch ZipandTabPheno.sh
sbatch ApaQTL_nominalNonnorm.sh

Concatinate files:

cat  TotalUsageChrom*.nominal.out > TotalUsageChrom_Nominal_AllChrom.txt
cat  NuclearUsageChrom*.nominal.out > NuclearUsageChrom_Nominal_AllChrom.txt

Pull out real total and nuc QLTs

python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/TotalUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt  

python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/NuclearUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/NuclearQTLinNuclearNominal_nonNorm.txt  


python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/TotalUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/TotalQTLinTotalNominal_nonNorm.txt  

python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/NuclearUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt  
totAPAinNuc=read.table("../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))


nucAPAinTot=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))

totAPAinTot=read.table("../data/QTLoverlap_nonNorm/TotalQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope) %>% dplyr::rename("Originalslope"=slope)

nucAPAinNuc=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope)%>% dplyr::rename("Originalslope"=slope)

Total

TotBoth= totAPAinNuc %>% inner_join(totAPAinTot,by=c("peakID", "snp"))

summary(lm(TotBoth$slope ~ TotBoth$Originalslope))

Call:
lm(formula = TotBoth$slope ~ TotBoth$Originalslope)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.34029 -0.07935 -0.01337  0.06956  0.46743 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)           0.019095   0.006562    2.91  0.00376 ** 
TotBoth$Originalslope 0.398243   0.013884   28.68  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1518 on 543 degrees of freedom
Multiple R-squared:  0.6024,    Adjusted R-squared:  0.6017 
F-statistic: 822.8 on 1 and 543 DF,  p-value: < 2.2e-16
totbothplot=ggplot(TotBoth, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", x="Effect size in Total",y="Effect size in Nucler") + geom_density_2d(col="red") + annotate("text", y=2, x=2, label="R2=.61, slope=0.4")

Nuclear

NucBoth= nucAPAinTot %>% inner_join(nucAPAinNuc,by=c("peakID", "snp"))
summary(lm(NucBoth$slope ~ NucBoth$Originalslope))

Call:
lm(formula = NucBoth$slope ~ NucBoth$Originalslope)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.74544 -0.03953  0.00153  0.04010  0.71657 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -0.001065   0.003189  -0.334    0.738    
NucBoth$Originalslope  0.742666   0.011220  66.193   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0998 on 1010 degrees of freedom
Multiple R-squared:  0.8127,    Adjusted R-squared:  0.8125 
F-statistic:  4381 on 1 and 1010 DF,  p-value: < 2.2e-16
Nucbothplot=ggplot(NucBoth, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") + geom_density_2d(col="red") +  annotate("text", y=2, x=1, label="R2=.81, slope=0.74")
plot_grid(totbothplot,Nucbothplot)

Version Author Date
f4a2106 brimittleman 2019-05-28

Remove Effect size > abs(1)

totAPAinNucFilt=read.table("../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% filter(abs(slope)<= 1)


nucAPAinTotFilt=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% filter(abs(slope)<= 1)

totAPAinTotFilt=read.table("../data/QTLoverlap_nonNorm/TotalQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope) %>%   filter(abs(slope)<= 1) %>% dplyr::rename("Originalslope"=slope)

nucAPAinNucFilt=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope)%>%  filter(abs(slope)<= 1) %>%dplyr::rename("Originalslope"=slope)
TotBothFilt= totAPAinNucFilt %>% inner_join(totAPAinTotFilt,by=c("peakID", "snp"))

summary(lm(TotBothFilt$slope ~ TotBothFilt$Originalslope))

Call:
lm(formula = TotBothFilt$slope ~ TotBothFilt$Originalslope)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.43593 -0.04769 -0.00197  0.05133  0.48722 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               0.004788   0.004634   1.033    0.302    
TotBothFilt$Originalslope 0.767422   0.018322  41.885   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1063 on 542 degrees of freedom
Multiple R-squared:  0.764, Adjusted R-squared:  0.7635 
F-statistic:  1754 on 1 and 542 DF,  p-value: < 2.2e-16
totbothplotfilt=ggplot(TotBothFilt, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", y="Effect size in Nuclear",x="Effect size in Total") + geom_density_2d(col="red") + annotate("text", y=.75, x=.1, label="R2=.76, slope=0.77")+ geom_abline(slope=1,color="green")
NucBothFilt= nucAPAinTotFilt %>% inner_join(nucAPAinNucFilt,by=c("peakID", "snp"))
summary(lm(NucBothFilt$slope ~ NucBothFilt$Originalslope))

Call:
lm(formula = NucBothFilt$slope ~ NucBothFilt$Originalslope)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.74819 -0.03867  0.00179  0.04084  0.37901 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0006196  0.0030789  -0.201    0.841    
NucBothFilt$Originalslope  0.7059759  0.0133986  52.690   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.09539 on 1007 degrees of freedom
Multiple R-squared:  0.7338,    Adjusted R-squared:  0.7336 
F-statistic:  2776 on 1 and 1007 DF,  p-value: < 2.2e-16
Nucbothplotfilt=ggplot(NucBothFilt, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", y="Effect size in Total",x="Effect size in Nuclear") + geom_density_2d(col="red") +  annotate("text", y=.75, x=.1, label="R2=.73, slope=0.71")+ geom_abline(slope=1,color="green")
plot_grid(totbothplotfilt,Nucbothplotfilt)

Box plots to look at the outliers:

get headers:

less /project2/gilad/briana/YRI_geno_hg19/chr3.dose.filt.vcf.gz | head -n14 | tail -n1 > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/genotypeHeader.txt
less /project2/gilad/briana/apaQTL/data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz | head -n1 > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/phenotypeHeader.txt

remove the hashtag in these

apaqtlfacetboxplots.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-G", "--geno"), action="store", default=NA, type='character',
              help="input genotype"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-p", "--peakID"), action="store", default=NA, type='character',
              help="peakID"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


phenohead=read.table("/project2/gilad/briana/apaQTL/data/ExampleQTLPlots/phenotypeHeader.txt", header = T,stringsAsFactors = F)
pheno=read.table(opt$pheno, col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(pheno, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/")  %>%   separate(chrom, into=c("chrom", "start", "end", "peakID"),sep=":") %>% mutate(PeakLoc=paste(start, end, sep=":"))

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)


genoHead=read.table("/project2/gilad/briana/apaQTL/data/ExampleQTLPlots/genotypeHeader.txt", header = T,stringsAsFactors = F)
geno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 


lettersGeno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F,colClasses = c("character")) %>% select(REF, ALT)

refAllele=lettersGeno$REF
altAllele=lettersGeno$ALT


genoMelt=melt(geno, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)


pheno_qtlpeak=meltpheno %>% inner_join(genoMelt, by="Individual") %>%  mutate(PAU=num/denom) 

qtlplot=ggplot(pheno_qtlpeak, aes(x=genotype, y=PAU, fill=genotype)) + geom_boxplot(width=.5)+ geom_jitter(alpha=1) + facet_grid(~PeakLoc) +scale_fill_brewer(palette = "YlOrRd")

ggsave(plot=qtlplot, filename=opt$output, height=10, width=10)

Code for boxplots:

run_qtlFacetBoxplots.sh

#!/bin/bash

#SBATCH --job-name=qtlFacetBoxplots
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=qtlFacetBoxplots.out
#SBATCH --error=qtlFacetBoxplots.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Fraction=$1
gene=$2
chrom=$3
snp=$4
peakID=$5


less /project2/gilad/briana/apaQTL/data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.${Fraction}.5perc.fc.gz | grep ${gene}_ > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksPheno.txt


less /project2/gilad/briana/YRI_geno_hg19/chr${chrom}.dose.filt.vcf.gz | grep ${snp} >  /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksGenotype.txt

Rscript apaqtlfacetboxplots.R -P /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksPheno.txt -G /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksGenotype.txt --gene ${gene} -p ${peakID}  -o /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}${SNP}${peakID}_boxplot.png

totAPAinTot %>% filter(abs(Originalslope)>1)
                        peakID         snp Originalslope
1      NBPF9_intron_-_peak7314 1:144701300       3.14235
2 HLA-DRB5_intron_+_peak113454  6:32486756    -207.01900
3    HLA-DRB6_end_+_peak113461  6:32538598       9.32331
sbatch run_qtlFacetBoxplots.sh "Total" "NBPF9" "1" "1:144701300" "peak7314"
sbatch run_qtlFacetBoxplots.sh "Total" "HLA-DRB5" "6" "6:32486756" "peak113454"
sbatch run_qtlFacetBoxplots.sh "Total" "HLA-DRB6" "6" "6:32538598" "peak113461"

nucAPAinNuc %>% filter(abs(Originalslope)>1)
                        peakID         snp Originalslope
1  LINC00869_intron_-_peak7883 1:149598905      -2.47583
2       FRG1BP_end_-_peak80905 20:29641550      -1.54150
3 HLA-DRB5_intron_+_peak113456  6:32468906       4.46867
sbatch run_qtlFacetBoxplots.sh "Nuclear" "LINC00869" "1" "1:149598905" "peak7883"
sbatch run_qtlFacetBoxplots.sh "Nuclear" "FRG1BP" "20" "20:29641550" "peak80905"
sbatch run_qtlFacetBoxplots.sh "Nuclear" "HLA-DRB5" "6" "6:32468906" "peak113456"


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

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

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

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

other attached packages:
 [1] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    MASS_7.3-51.1    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4