Last updated: 2019-08-30
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Knit directory: apaQTL/analysis/
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html | 1d8fbcb | brimittleman | 2019-08-29 | Build site. |
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 ──
✔ 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(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')
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
-2.8866 -1.0886 0.4571 0.7981 2.2386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.38684 0.07066 5.475 1.15e-07 ***
bothNucspecandE$Eslope -0.11181 0.23729 -0.471 0.638
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.071 on 228 degrees of freedom
Multiple R-squared: 0.0009729, Adjusted R-squared: -0.003409
F-statistic: 0.222 on 1 and 228 DF, p-value: 0.6379
cor.test(bothNucspecandE$slope,bothNucspecandE$Eslope, alternative="less")
Pearson's product-moment correlation
data: bothNucspecandE$slope and bothNucspecandE$Eslope
t = -0.4712, df = 228, p-value = 0.319
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
-1.00000000 0.07781426
sample estimates:
cor
-0.03119076
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")
Version | Author | Date |
---|---|---|
1d8fbcb | brimittleman | 2019-08-29 |
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