Last updated: 2018-11-29
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 413c8fd | Briana Mittleman | 2018-11-29 | add filter QTL analysis and start explain pqtl |
In this analysis I want to look at how well the eQTLs and apaQTLs an explain pQTLs. I will use linear models on the effect sizes.
Libraries
library(workflowr)This is workflowr version 1.1.1
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.0.0 ✔ purrr 0.2.5
✔ tibble 1.4.2 ✔ dplyr 0.7.6
✔ tidyr 0.8.1 ✔ stringr 1.3.1
✔ readr 1.1.1 ✔ forcats 0.3.0── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithslibrary(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveInput the pQTLs (10% FDR) and gene names
geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F)
pQTL=read.table("../data/other_qtls/fastqtl_qqnorm_prot.fixed.perm.out", col.names = c("Gene.stable.ID", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"), header = F, stringsAsFactors = F) %>% inner_join(geneNames, by="Gene.stable.ID") %>% dplyr::select("Gene.name","Gene.stable.ID", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval")
pQTL$bh=p.adjust(pQTL$bpval, method="fdr")
pQTL_sig=pQTL %>% filter(-log10(bh)> 1) Start with the exanple with the highest effect size:
CCDC51- ENSG00000164051 3:48476431
I need all of the results for this snp gene pair from the total, nuclear, and RNA nominal files. I can make a python script that will take the gene name and snp and create the relevent dataframe.
files: * /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out (need the ENSG ID)
APAandRNAfromProtQTLs.py
#use this by inserting a gene, gene_ensg (from prot), and snp for the protien QTLs
def main(gene, gene_ensg,snp):
out_RNA=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_RNAres.txt"%(gene, snp), "w")
out_Total=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Totalres.txt"%(gene, snp), "w")
out_Nulcear=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Nuclearres.txt"%(gene, snp), "w")
for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", "r"):
s=ln.split()[1]
g=ln.split()[0].split(":")[3].split("_")[0]
if g==gene and s==snp:
out_Nuclear.write(ln)
for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", "r"):
s=ln.split()[1]
g=ln.split()[0].split(":")[3].split("_")[0]
if g==gene and s==snp:
out_Total.write(ln)
for ln in open("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out", "r"):
s=ln.split()[1]
g=ln.split()[0]
if g in gene and s==snp:
out_RNA.write(ln)
out_Total.close()
out_Nuclear.close()
out_RNA.close()
if __name__ == "__main__":
import sys
gene=sys.argv[1]
gene_ensg=sys.argv[2]
snp=sys.argv[3]
main(gene, gene_ensg, snp)
APAandRNAfromProtQTLs.py CCDC51 ENSG00000164051 3:48476431I first want to look at the effect sizes. I am interested in understanding the directions of the effect sizes.
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 cowplot_0.9.3 reshape2_1.4.3 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[9] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0 tidyverse_1.2.1
[13] workflowr_1.1.1
loaded via a namespace (and not attached):
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[4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.2.0
[7] rlang_0.2.2 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.7.0
[13] modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[22] evaluate_0.11 knitr_1.20 broom_0.5.0
[25] Rcpp_0.12.19 scales_1.0.0 backports_1.1.2
[28] jsonlite_1.5 hms_0.4.2 digest_0.6.17
[31] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[34] cli_1.0.1 tools_3.5.1 magrittr_1.5
[37] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
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[43] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[46] rstudioapi_0.8 R6_2.3.0 nlme_3.1-137
[49] git2r_0.23.0 compiler_3.5.1
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