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Last updated: 2019-08-28

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PAS_graphs.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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

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

File	Version	Author	Date	Message
Rmd	22e9f6a	brimittleman	2019-08-28	add explained
html	aacf73d	brimittleman	2019-08-28	Build site.
Rmd	1c31e02	brimittleman	2019-08-28	add proportion expained
html	a850a6e	brimittleman	2019-08-28	Build site.
Rmd	2f37837	brimittleman	2019-08-28	add unexaplined and pqtl res

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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✔ 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()

mkdir ../data/Version15bp6As
mkdir ../data/Version15bp7As

I am going to test 2 filtering versions. I will run these in parallel here. I will put each step of the analysis in the directories above. I will start in the SnakefileFiltPAS with the named SAF file. I will need to convert this to a bed file to use bedtools nuc. I will then filter the final SAF and run the quantification.

These are still on the opposite strand. I will look at the 15 bases upstream of each PAS for T’s.

For + strand: startnew=start-15 endnew=start

for - strand: startnew=end endnew=end +15

mkdir ../data/Version15bp6As/filter15upfiles
mkdir ../data/Version15bp7As/filter15upfiles

Filter

python SAF215upbed.py ../data/Version15bp6As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15up.bed
python SAF215upbed.py ../data/Version15bp7As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15up.bed

Run bedtools nuc on these:

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/Version15bp6As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15up.bed > ../data/Version15bp6As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC.txt

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/Version15bp7As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15up.bed > ../data/Version15bp7As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC.txt

Filter out 6 or 7 A. I will do this by making a dictionary with the Ok and outputting only the SAF file PAS in this dictionary.

I will make a script that takes the input, output, the number of A’s to filter

python filterSAFforMP.py ../data/Version15bp6As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC.txt ../data/Version15bp6As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC6A.SAF 6

python filterSAFforMP.py ../data/Version15bp7As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC.txt ../data/Version15bp7As/filter15upfiles/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC7A.SAF 7

Now i can run feature counts for these files:

mkdir ../data/Version15bp6As/peakCoverage/
mkdir ../data/Version15bp7As/peakCoverage/
sbatch fc_filteredPAS6and7As.sh

python fixFChead_short.py ../data/Version15bp6As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC6A.Nuclear.Quant.fc ../data/Version15bp6As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC6A.Nuclear.Quant.fixed.fc

python fixFChead_short.py  ../data/Version15bp7As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC7A.Nuclear.Quant.fc ../data/Version15bp7As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC7A.Nuclear.Quant.fixed.fc

Make phenotype

mkdir ../data/Version15bp6As/phenotype/
mkdir ../data/Version15bp7As/phenotype/
  
  
python makePheno.py ../data/Version15bp6As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC6A.Nuclear.Quant.fixed.fc ../data/peakCoverage/file_id_mapping_Nuclear_Transcript.txt  ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.fc


python makePheno.py ../data/Version15bp7As/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.15upNUC7A.Nuclear.Quant.fixed.fc ../data/peakCoverage/file_id_mapping_Nuclear_Transcript.txt ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.fc

Rscript pheno2countonly.R -I ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.fc -O ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.CountsOnly

Rscript pheno2countonly.R -I ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.fc -O ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.CountsOnly

python convertNumeric.py ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.CountsOnly ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.CountsOnlyNumeric

python convertNumeric.py ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.CountsOnly ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.CountsOnlyNumeric

mkdir ../data/Version15bp7As/peaks_5perc/
mkdir ../data/Version15bp6As/peaks_5perc/

Rscript filter5perc.R -P ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.fc -N ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.CountsOnlyNumeric -O  ../data/Version15bp6As/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp6Aperc.fc
 
 Rscript filter5perc.R -P ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.fc -N ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.CountsOnlyNumeric -O  ../data/Version15bp7As/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp7Aperc.fc
 
mkdir ../data/Version15bp7As/phenotype_5perc/
mkdir ../data/Version15bp6As/phenotype_5perc/


 python filter5percPheno.py ../data/Version15bp6As/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp6Aperc.fc ../data/Version15bp6As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.fc ../data/Version15bp6As/phenotype_5perc/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc
 
  python filter5percPheno.py ../data/Version15bp7As/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp7Aperc.fc ../data/Version15bp7As/phenotype/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.fc ../data/Version15bp7As/phenotype_5perc/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc
  
  
  #cut -f1-3,7,8,6 -d " " APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp6Aperc.fc > APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp6Aperc.bed
  
 # cut -f1-3,7,8,6 -d " " APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp7Aperc.fc > APApeak_Peaks_GeneLocAnno.Nuclear.5_15bp7Aperc.bed

QTL analysis

module load python

gzip ../data/Version15bp6As/phenotype_5perc/APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc
gzip ../data/Version15bp7As/phenotype_5perc/APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc

#do in dir
python ../../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc.gz
python ../../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc.gz



sh APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc.gz_prepare.sh
head -n 5 APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno_15bp6A.Nuclear.5perc.fc.gz.4PCs


sh APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc.gz_prepare.sh
head -n 5 APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno_15bp7A.Nuclear.5perc.fc.gz.4PCs


mkdir ../data/Version15bp6As/apaQTLPermuted
mkdir ../data/Version15bp6As/apaQTLNominal
mkdir ../data/Version15bp7As/apaQTLPermuted
mkdir ../data/Version15bp7As/apaQTLNominal

sbatch apaQTL_permuted_test6A7A.sh
sbatch apaQTL_nominalv67.sh



cat ../data/Version15bp6As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp6A.5perc.fc.gz.qqnorm_chr* > ../data/Version15bp6As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp6A.5perc_permRes.txt
cat ../data/Version15bp7As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp7A.5perc.fc.gz.qqnorm_chr* > ../data/Version15bp7As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp7A.5perc_permRes.txt

cat ../data/Version15bp6As/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp6A.5perc.fc.gz.qqnorm_chr* >../data/Version15bp6As/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp6A.5perc_nomRes.txt
cat ../data/Version15bp7As/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp7A.5perc.fc.gz.qqnorm_chr* >../data/Version15bp7As/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp7A.5perc_nomRes.txt

Rscript apaQTLCorrectedpval_6or7a.R

QTL6A=read.table("../data/Version15bp6As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp6A.5perc_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

QTL6ASog= QTL6A %>% filter(-log10(bh)>=1)
nrow(QTL6ASog)

[1] 576

QTL7A=read.table("../data/Version15bp7As/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_15bp7A.5perc_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

QTL7Sog= QTL7A %>% filter(-log10(bh)>=1)
nrow(QTL7Sog)

[1] 586

Explained v unexplained QTL


mkdir ../data/Version15bp6As/ApaByEgene
mkdir ../data/Version15bp7As/ApaByEgene

python subsetpermAPAwithGenelist_2versions.py ../data/Li_eQTLs/explainedEgenes.txt 6 ../data/Version15bp6As/ApaByEgene/ApaexplainedeGenes.txt
python subsetpermAPAwithGenelist_2versions.py ../data/Li_eQTLs/explainedEgenes.txt 7 ../data/Version15bp7As/ApaByEgene/ApaexplainedeGenes.txt

python subsetpermAPAwithGenelist_2versions.py ../data/Li_eQTLs/UnexplainedEgenes.txt 6 ../data/Version15bp6As/ApaByEgene/ApaUnaexplainedeGenes.txt
python subsetpermAPAwithGenelist_2versions.py ../data/Li_eQTLs/UnexplainedEgenes.txt 7 ../data/Version15bp7As/ApaByEgene/ApaUnexplainedeGenes.txt


python subsetApanoteGene_2versions.py 6 ../data/Version15bp6As/ApaByEgene/ApaNOTeGene.txt
python subsetApanoteGene_2versions.py 7 ../data/Version15bp7As/ApaByEgene/ApaNOTeGene.txt

6As

six.notE=read.table("../data/Version15bp6As/ApaByEgene/ApaNOTeGene.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
six.ex=read.table("../data/Version15bp6As/ApaByEgene/ApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
six.un=read.table("../data/Version15bp6As/ApaByEgene/ApaUnaexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
six.un=na.omit(six.un)

qqplot(-log10(runif(nrow(six.notE))), -log10(six.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear 6A Apa")
points(sort(-log10(runif(nrow(six.ex)))), sort(-log10(six.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(six.un)))), sort(-log10(six.un$bpval)),col= alpha("Blue"))
abline(0,1)

legend("topleft", legend=c("Not eGenes", "Explained eGenes", "Unexplained eGenes"),col=c("black", "red", "blue"), pch=16,bty = 'n')

Version	Author	Date
a850a6e	brimittleman	2019-08-28

7As

seven.notE=read.table("../data/Version15bp7As/ApaByEgene/ApaNOTeGene.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
seven.ex=read.table("../data/Version15bp7As/ApaByEgene/ApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
seven.un=read.table("../data/Version15bp7As/ApaByEgene/ApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
seven.un=na.omit(seven.un)

qqplot(-log10(runif(nrow(seven.notE))), -log10(seven.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear 7A Apa")
points(sort(-log10(runif(nrow(seven.ex)))), sort(-log10(seven.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(seven.un)))), sort(-log10(seven.un$bpval)),col= alpha("Blue"))
abline(0,1)

legend("topleft", legend=c("Not eGenes", "Explained eGenes", "Unexplained eGenes"),col=c("black", "red", "blue"), pch=16,bty = 'n')

Version	Author	Date
a850a6e	brimittleman	2019-08-28

python convertNominal2SNPloc2Versions.py 6
python convertNominal2SNPloc2Versions.py 7

mkdir ../data/Version15bp6As/overlapeQTL
mkdir ../data/Version15bp7As/overlapeQTL

sbatch run_getAPAfromeQTL_version6.7.sh

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
SixapaUnexplained=read.table("../data/Version15bp6As/overlapeQTL/apa_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

SixapaUnexplained_sig= SixapaUnexplained %>% filter(adjPval<.05)

nrow(SixapaUnexplained_sig)/nrow(SixapaUnexplained)

[1] 0.1518771

SixapaExplained=read.table("../data/Version15bp6As/overlapeQTL/apa_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

SixapaExplained_sig= SixapaExplained %>% filter(adjPval<.05)

nrow(SixapaExplained_sig)/nrow(SixapaExplained)

[1] 0.126071

SevenapaUnexplained=read.table("../data/Version15bp7As/overlapeQTL/apa_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

SevenapaUnexplained_sig= SevenapaUnexplained %>% filter(adjPval<.05)

nrow(SevenapaUnexplained_sig)/nrow(SevenapaUnexplained)

[1] 0.1482112

SevenapaExplained=read.table("../data/Version15bp7As/overlapeQTL/apa_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

SevenapaExplained_sig= SevenapaExplained %>% filter(adjPval<.05)

nrow(SevenapaExplained_sig)/nrow(SevenapaExplained)

[1] 0.1221001

Pqtl

mkdir ../data/Version15bp6As/ApaByPgene
mkdir ../data/Version15bp7As/ApaByPgene

python subsetpermAPAwithGenelist_2versions.py ../data/Battle_pQTL/psQTLGeneNames.txt 6 ../data/Version15bp6As/ApaByPgene/ApaPSGenes.txt

python subsetpermAPAwithGenelist_2versions.py ../data/Battle_pQTL/esQTLGenes.txt 6 ../data/Version15bp6As/ApaByPgene/ApaESGenes.txt

python subsetpermAPAwithGenelist_2versions.py ../data/Battle_pQTL/psQTLGeneNames.txt 7 ../data/Version15bp7As/ApaByPgene/ApaPSGenes.txt

python subsetpermAPAwithGenelist_2versions.py ../data/Battle_pQTL/esQTLGenes.txt 7 ../data/Version15bp7As/ApaByPgene/ApaESGenes.txt

python subsetAPAnotEorPgene_2versions.py 6 ../data/Version15bp6As/ApaByPgene/ApaNOTPorEGenes.txt
python subsetAPAnotEorPgene_2versions.py 7 ../data/Version15bp7As/ApaByPgene/ApaNOTPorEGenes.txt

6As

six.notEorP=read.table("../data/Version15bp6As/ApaByPgene/ApaNOTPorEGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
six.PS=read.table("../data/Version15bp6As/ApaByPgene/ApaPSGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
six.ES=read.table("../data/Version15bp6As/ApaByPgene/ApaESGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )


six_allE=as.data.frame(rbind(six.ex,six.un))
six.PS=na.omit(six.PS)
six.notEorP=na.omit(six.notEorP)
six.ES=na.omit(six.ES)
six.un=na.omit(six.un)

qqplot(-log10(runif(nrow(six.notEorP))), -log10(six.notEorP$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="6A Nuclear Apa")
points(sort(-log10(runif(nrow(six.PS)))), sort(-log10(six.PS$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(six_allE)))), sort(-log10(six_allE$bpval)),col= alpha("blue"))
abline(0,1)

legend("topleft", legend=c("Neither eGenes nor pGenes", "pGenes", "eGenes"),col=c("black", "red","blue"), pch=16,bty = 'n')

Version	Author	Date
aacf73d	brimittleman	2019-08-28

7As

seven.notEorP=read.table("../data/Version15bp7As/ApaByPgene/ApaNOTPorEGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
seven.PS=read.table("../data/Version15bp7As/ApaByPgene/ApaPSGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
seven.ES=read.table("../data/Version15bp7As/ApaByPgene/ApaESGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )


seven_allE=as.data.frame(rbind(seven.ex,seven.un))
seven.PS=na.omit(seven.PS)
seven.notEorP=na.omit(seven.notEorP)
seven.ES=na.omit(seven.ES)
seven.un=na.omit(seven.un)

qqplot(-log10(runif(nrow(seven.notEorP))), -log10(seven.notEorP$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="7A Nuclear Apa")
points(sort(-log10(runif(nrow(seven.PS)))), sort(-log10(seven.PS$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(seven_allE)))), sort(-log10(seven_allE$bpval)),col= alpha("blue"))
abline(0,1)

legend("topleft", legend=c("Neither eGenes nor pGenes", "pGenes", "eGenes"),col=c("black", "red","blue"), pch=16,bty = 'n')

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    ggplot2_3.1.1  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        workflowr_1.4.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4