Nuclear specific in eQTLs

Last updated: 2019-08-29

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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/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/version15bpfilter.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	a6705c8	brimittleman	2019-08-29	add effect size corr for nuc specific
html	2fb467b	brimittleman	2019-07-07	Build site.
Rmd	bd5f228	brimittleman	2019-07-07	add nuc spec eqtl res

I want to make a qq plot for the eQTL results then seperate the values by genes with a nuclear specific apaQTL.

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()

library(workflowr)

This is workflowr version 1.4.0
Run ?workflowr for help getting started

First i need to run the permutations on the eQTL data to get the top snp gene associations.

sbatch EandPqtl_perm.sh

cat ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.chunk* > ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.txt

python changePermQTLres2geneName.py

permeqtl=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.GeneName.txt", col.names = c("gene", "nvar","shape1", "shape2", "dummy", "RSID", "dist","nomPval","slope","ppval", "bpval"),stringsAsFactors = F)

permeqtl$bh=p.adjust(permeqtl$bpval, method="fdr")

qqplot(-log10(runif(nrow(permeqtl))), -log10(permeqtl$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
abline(0,1)

Version	Author	Date
2fb467b	brimittleman	2019-07-07

Seperate for genes with a nuclear specific apaQTL

nuclear specific (only tested in nuclear) are in ../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt

nuclear specific not sig in total:

nucSpecgene=read.table("../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt",header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "Originalslope")) %>% separate(peakID,into=c("gene","loc", "strand", "PAS"),sep="_") %>% select(gene) %>% unique()

nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% separate(peakID,into=c("chr", "start", "end", "geneID"),sep=":" ) %>% separate(geneID, into=c("gene", "loc", "strand", "peakNum"), sep="_")
nucAPAinTot_NOTSIG=nucAPAinTot %>% filter(pval>.05) %>% select(gene) %>% unique()

allNucspec=nucAPAinTot_NOTSIG %>% full_join(nucSpecgene,by="gene")

There are 172 genes in this set.

permeqtl_nucSpec= permeqtl  %>% filter(gene %in%allNucspec$gene )

permeqtl_notnucSpec= permeqtl  %>% anti_join(permeqtl_nucSpec,by = c("gene", "nvar", "shape1", "shape2", "dummy", "RSID", "dist", "nomPval", "slope", "ppval", "bpval", "bh"))

qqplot(-log10(runif(nrow(permeqtl_notnucSpec))), -log10(permeqtl_notnucSpec$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
points(sort(-log10(runif(nrow(permeqtl_nucSpec)))), sort(-log10(permeqtl_nucSpec$bpval)),col= alpha("Red"))

abline(0,1)
legend("topleft", legend=c("Nuclear specific apaQTL genes", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version	Author	Date
2fb467b	brimittleman	2019-07-07

wilcox.test(permeqtl_notnucSpec$ppval,permeqtl_nucSpec$ppval)


    Wilcoxon rank sum test with continuity correction

data:  permeqtl_notnucSpec$ppval and permeqtl_nucSpec$ppval
W = 194420, p-value = 0.1278
alternative hypothesis: true location shift is not equal to 0

Compare effect sizes:

#allNucspec is the nuclear specific gene, will pull these genes out 

nuclearQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", header = T,stringsAsFactors = F) %>% rename("gene"= Gene) %>% semi_join(allNucspec, by="gene")

write.table(nuclearQTLs, file="../data/apaQTLs/NuclearSpecificAPAqtls.txt", col.names = T, row.names = F, quote = F, sep="\t")

python nucSpecQTLineData.py

Join these by snp and gene.

nomNamesE=c("gene", "sid",'dist', 'pval', 'Eslope' )
eQTLforNucspec=read.table("../data/eQTLs/NuclearSPecificAPAqtlsinEqtl.txt",stringsAsFactors = F, col.names = nomNamesE)

bothNucspecandE=eQTLforNucspec %>% inner_join(nuclearQTLs, by=c("gene", "sid"))

summary(lm(bothNucspecandE$slope~bothNucspecandE$Eslope))


Call:
lm(formula = bothNucspecandE$slope ~ bothNucspecandE$Eslope)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3500 -1.0234  0.3407  0.7581  1.5966 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.4431     0.1031   4.299  4.3e-05 ***
bothNucspecandE$Eslope  -0.5343     0.3137  -1.703    0.092 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9909 on 91 degrees of freedom
Multiple R-squared:  0.03089,   Adjusted R-squared:  0.02024 
F-statistic: 2.901 on 1 and 91 DF,  p-value: 0.09196

cor.test(bothNucspecandE$slope,bothNucspecandE$Eslope, alternative="less")


    Pearson's product-moment correlation

data:  bothNucspecandE$slope and bothNucspecandE$Eslope
t = -1.7031, df = 91, p-value = 0.04598
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
 -1.000000000 -0.004219134
sample estimates:
       cor 
-0.1757579

ggplot(bothNucspecandE, aes(x=Eslope, y=slope)) + geom_point() + geom_smooth(method="lm") + labs(x="eQTL effect size", y="apaQTL effect size", title="Effect size in eQTL for nuclear specific apaQTL")

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] workflowr_1.4.0 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

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        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    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

Nuclear specific in eQTLs

Briana Mittleman

7/7/2019