Last updated: 2018-09-06
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 98159a7 | Briana Mittleman | 2018-09-06 | alpha .3 |
html | febabe4 | Briana Mittleman | 2018-09-06 | Build site. |
Rmd | 57005d9 | Briana Mittleman | 2018-09-06 | make qqplot |
html | 584548c | Briana Mittleman | 2018-09-06 | Build site. |
Rmd | e2f5e81 | Briana Mittleman | 2018-09-06 | fix run code |
html | f92a58f | Briana Mittleman | 2018-09-06 | Build site. |
Rmd | 46b7343 | Briana Mittleman | 2018-09-06 | add overlap analysis with code to subset |
I will use this to overlap my QTLs with the other molecular QTLs already identified in the same individuals. First pass I will subset my nuclear and total nomial qtls by the snps with pvals less than .05 in each of the sets and make a qqplot.
I want to create a python script that takes in which type of qtl and a pvalue and subsets the full file for snps that pass that filter.
subset_qtls.py
def main(inFile, outFile, qtl, cutoff):
fout=open(outFile, "w")
ifile=open(inFile, "r")
cutoff=float(cutoff)
qtl_types= ['4su_30', '4su_60', 'RNAseq', 'RNAseqGeuvadis', 'ribo', 'prot']
if qtl not in qtl_types:
raise NameError("QTL arg must be 4su_30, 4su_60, RNAseq, RNAseqGeuvadis, ribo, or prot")
elif qtl=="4su_30":
target=4
elif qtl=="4su_60":
target=5
elif qtl=="RNAseq":
target=6
elif qtl=="RNAseqGeuvadis":
target=7
elif qtl=="ribo":
target =8
elif qtl=="prot":
target=9
for num,ln in enumerate(ifile):
if num > 0 :
line_list = ln.split()
chrom=line_list[0][3:]
pos=line_list[1]
rsid=line_list[2]
geneID=line_list[3]
val = line_list[target].split(":")[0]
if val == "NA":
continue
else:
val = float(val)
if val <= cutoff:
fout.write("%s:%s\t%s\t%s\t%f\n"%(chrom, pos, rsid, geneID,val))
if __name__ == "__main__":
import sys
qtl = sys.argv[1]
cutoff= sys.argv[2]
inFile = "/project2/gilad/briana/threeprimeseq/data/otherQTL/summary_betas_ste_100kb.txt"
outFile = "/project2/gilad/briana/threeprimeseq/data/otherQTL/summary_betas_ste_100kb.%s%s.txt"%(qtl, cutoff)
main(inFile, outFile, qtl, cutoff)
I can run this to subset by each qtl at .05
run_subsetQTLs05.sh
#!/bin/bash
#SBATCH --job-name=run_subsetqtl05
#SBATCH --account=pi-gilad
#SBATCH --time=24:00:00
#SBATCH --output=run_subsetqtl05.out
#SBATCH --error=run_subsetqtl05.err
#SBATCH --partition=gilad
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
#qtls=('4su_30', '4su_60', 'RNAseq', 'RNAseqGeuvadis', 'ribo', 'prot')
for i in 4su_30 4su_60 RNAseq RNAseqGeuvadis ribo prot; do
python subset_qtls.py $i .05
done
library(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(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
library(readr)
nuc.nom=read.table("../data/nom_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes_onetenth.txt", header = F, stringsAsFactors = F)
colnames(nuc.nom)= c("peakID", "snpID", "dist", "nuc_pval", "slope")
QTL_names=c("snpID", "snpid2","Gene", "pval")
fourSU30= read.table("../data/other_qtls/summary_betas_ste_100kb.4su_30.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
fourSU60=read.table("../data/other_qtls/summary_betas_ste_100kb.4su_60.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
RNAseq=read.table("../data/other_qtls/summary_betas_ste_100kb.RNAseq.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
guevardis=read.table("../data/other_qtls/summary_betas_ste_100kb.RNAseqGeuvadis.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
ribo=read.table("../data/other_qtls/summary_betas_ste_100kb.ribo.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
prot=read.table("../data/other_qtls/summary_betas_ste_100kb.prot.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)
Overlap the files:
fourSU30AndNuc= fourSU30 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
fourSU30_unif=runif(nrow(fourSU30AndNuc))
fourSU60AndNuc= fourSU60 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
fourSU60_unif=runif(nrow(fourSU60AndNuc))
RNAAndNuc= RNAseq %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
RNAseq_unif=runif(nrow(RNAAndNuc))
GuevAndNuc= guevardis %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
guev_unif=runif(nrow(GuevAndNuc))
riboAndNuc= ribo %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
ribo_unif=runif(nrow(riboAndNuc))
protAndNuc= prot %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
prot_unif=runif(nrow(protAndNuc))
Plot results:
qqplot(-log10(runif(nrow(nuc.nom))), -log10(nuc.nom$nuc_pval),ylab="-log10 Nuclear nominal pvalue", xlab="Uniform expectation", main="Nuclear Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif)), sort(-log10(fourSU30AndNuc$nuc_pval)), col="Red", alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
points(sort(-log10(fourSU60_unif)), sort(-log10(fourSU60AndNuc$nuc_pval)), col="Orange",alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
points(sort(-log10(RNAseq_unif)), sort(-log10(RNAAndNuc$nuc_pval)), col="Yellow",alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
points(sort(-log10(guev_unif)), sort(-log10(GuevAndNuc$nuc_pval)), col="Green",alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
points(sort(-log10(ribo_unif)), sort(-log10(riboAndNuc$nuc_pval)), col="Blue",alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
points(sort(-log10(prot_unif)), sort(-log10(protAndNuc$nuc_pval)), col="Purple",alpha=.3)
Warning in plot.xy(xy.coords(x, y), type = type, ...): "alpha" is not a
graphical parameter
abline(0,1)
legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Protein"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)
Version | Author | Date |
---|---|---|
febabe4 | Briana Mittleman | 2018-09-06 |
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
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] reshape2_1.4.3 workflowr_1.1.1 forcats_0.3.0 stringr_1.3.1
[5] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1 tidyr_0.8.1
[9] tibble_1.4.2 ggplot2_3.0.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[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 bindrcpp_0.2.2
[16] bindr_0.1.1 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[22] R.methodsS3_1.7.1 evaluate_0.11 knitr_1.20
[25] broom_0.5.0 Rcpp_0.12.18 scales_1.0.0
[28] backports_1.1.2 jsonlite_1.5 hms_0.4.2
[31] digest_0.6.16 stringi_1.2.4 grid_3.5.1
[34] rprojroot_1.3-2 cli_1.0.0 tools_3.5.1
[37] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[40] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[43] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[46] httr_1.3.1 rstudioapi_0.7 R6_2.2.2
[49] nlme_3.1-137 git2r_0.23.0 compiler_3.5.1
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