Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Emain-1.pdf
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Esubset-1.pdf
    Modified:   docs/figure/chromHHMQTL.Rmd/figure3D-1.pdf
    Deleted:    docs/figure/exvunexpeQTL.Rmd/figure3C-1.pdf
    Deleted:    reads_graphs.Rmd

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library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

I want to ask if the unexplained and explaiend eQTLs are in the same or different genes.

explainedGenes=read.table("../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt", stringsAsFactors = F, col.names = c("chr", "snp", "gene") )%>% select(gene) %>% unique() 

write.table(explainedGenes, file="../data/Li_eQTLs/explainedEgenes.txt", col.names = F, row.names = F, quote = F, sep="\t")

UNexplainedGenes=read.table("../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt", stringsAsFactors = F, col.names = c("chr", "snp", "gene") )%>% select(gene) %>% unique()
write.table(UNexplainedGenes, file="../data/Li_eQTLs/UnexplainedEgenes.txt",col.names = F, row.names = F, quote = F, sep="\t")

Make these vectors:

explainedGenesVec=explainedGenes$gene
UNexplainedGenesVec=UNexplainedGenes$gene

Overlap:

both <- UNexplainedGenesVec[UNexplainedGenesVec %in% explainedGenesVec]
both
character(0)

Plot the permuted pvalues for apa in 3 seperate lines. not eGenes, unexplained egenes, explained egenes

I want to make a script that takes these genes and a fraction and will give only the permuted apa values for that set. I will also make a script that will return the values for genes in neither set.

mkdir ../data/ApaByEgene
python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/explainedEgenes.txt Total ../data/ApaByEgene/TotalApaexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/UnexplainedEgenes.txt Total ../data/ApaByEgene/TotalApaUnexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/explainedEgenes.txt Nuclear ../data/ApaByEgene/NuclearApaexplainedeGenes.txt

python subsetpermAPAwithGenelist.py ../data/Li_eQTLs/UnexplainedEgenes.txt Nuclear ../data/ApaByEgene/NuclearApaUnexplainedeGenes.txt


python subsetApanoteGene.py Total ../data/ApaByEgene/TotalApaNOTeGene.txt
python subsetApanoteGene.py Nuclear ../data/ApaByEgene/NuclearApaNOTeGene.txt

Make QQplots with these:

Total

tot.notE=read.table("../data/ApaByEgene/TotalApaNOTeGene.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.ex=read.table("../data/ApaByEgene/TotalApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
tot.un=read.table("../data/ApaByEgene/TotalApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
tot.un=na.omit(tot.un)
qqplot(-log10(runif(nrow(tot.notE))), -log10(tot.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Total Apa")
points(sort(-log10(runif(nrow(tot.ex)))), sort(-log10(tot.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(tot.un)))), sort(-log10(tot.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
a5aab43 brimittleman 2019-06-13
d1e8082 brimittleman 2019-06-12
4c50025 brimittleman 2019-06-06 
wilcox.test(tot.notE$bpval,tot.ex$bpval,alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  tot.notE$bpval and tot.ex$bpval
W = 29161000, p-value = 1.526e-07
alternative hypothesis: true location shift is greater than 0
wilcox.test(tot.notE$bpval,tot.un$bpval, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  tot.notE$bpval and tot.un$bpval
W = 21642000, p-value = 1.488e-05
alternative hypothesis: true location shift is greater than 0
wilcox.test(tot.un$bpval,tot.ex$bpval, alternative = "less")

    Wilcoxon rank sum test with continuity correction

data:  tot.un$bpval and tot.ex$bpval
W = 1760400, p-value = 0.5706
alternative hypothesis: true location shift is less than 0

Nuclear

nuc.notE=read.table("../data/ApaByEgene/NuclearApaNOTeGene.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.ex=read.table("../data/ApaByEgene/NuclearApaexplainedeGenes.txt",stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.un=read.table("../data/ApaByEgene/NuclearApaUnexplainedeGenes.txt",stringsAsFactors = F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval") )
nuc.un=na.omit(nuc.un)
qqplot(-log10(runif(nrow(nuc.notE))), -log10(nuc.notE$bpval), xlab="-log10(Uniform)", ylab="-log10(beta pval)", main="Nuclear Apa")
points(sort(-log10(runif(nrow(nuc.ex)))), sort(-log10(nuc.ex$bpval)),col= alpha("Red"))
points(sort(-log10(runif(nrow(nuc.un)))), sort(-log10(nuc.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
ab8482d brimittleman 2019-08-01
416f8e8 brimittleman 2019-06-27 
wilcox.test(nuc.notE$bpval,nuc.ex$bpval,alternative="greater")

    Wilcoxon rank sum test with continuity correction

data:  nuc.notE$bpval and nuc.ex$bpval
W = 37769000, p-value = 3.865e-05
alternative hypothesis: true location shift is greater than 0
wilcox.test(nuc.notE$bpval,nuc.un$bpval,alternative="greater")

    Wilcoxon rank sum test with continuity correction

data:  nuc.notE$bpval and nuc.un$bpval
W = 29617000, p-value = 6.048e-11
alternative hypothesis: true location shift is greater than 0
wilcox.test(nuc.un$bpval,nuc.ex$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  nuc.un$bpval and nuc.ex$bpval
W = 2264400, p-value = 0.01159
alternative hypothesis: true location shift is less than 0

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