Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/NewPeakPostMP.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
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Modified: analysis/dif.iso.usage.leafcutter.Rmd
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Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlapMolQTL.opposite.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/peakQCPPlots.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/pipeline_55Ind.Rmd
Modified: analysis/swarmPlots_QTLs.Rmd
Modified: analysis/test.max2.Rmd
Modified: analysis/test.smash.Rmd
Modified: analysis/understandPeaks.Rmd
Modified: code/Snakefile
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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | ea3cfeb | Briana Mittleman | 2018-09-26 | Build site. |
Rmd | 1c62f0b | Briana Mittleman | 2018-09-26 | add distribution of distance |
html | cd3bdf8 | Briana Mittleman | 2018-09-26 | Build site. |
Rmd | 529ace6 | Briana Mittleman | 2018-09-26 | add QTL res |
html | b1bcf99 | Briana Mittleman | 2018-09-25 | Build site. |
Rmd | f4e1942 | Briana Mittleman | 2018-09-25 | initiate all ind QTL analysis |
library(tidyverse)
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library(reshape2)
Attaching package: 'reshape2'
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smiths
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
I am using the code from peakOverlap_oppstrand.Rmd analysis to call QTLs on the full set of individuals. (still missing 4 due to genotype issues- Remove 18500, 19092 and 19193, 18497 - at 35).
Scripts:
* APAqtl_nominal_oppstrand.sh
cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt
cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt
Write a script to ad the BH correction of the permuted QTL pvalues. I will write the plots to
APAqtlpermCorrectQQplot.R
library(dplyr)
##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")
#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm.pdf")
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()
#write df with BH
write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", col.names = T, row.names = F, quote = F)
##nuclear results
nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")
#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm.pdf")
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()
# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", col.names = T, row.names = F, quote = F)
Write a script to run this:
run_APAqtlpermCorrectQQplot.sh
#!/bin/bash
#SBATCH --job-name=run_APAqtlpermCorrectQQplot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot.out
#SBATCH --error=run_APAqtlpermCorrectQQplot.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
Rscript APAqtlpermCorrectQQplot.R
tot_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", header=T, stringsAsFactors = F)
Check to quality of the tests:
plot(tot_permBH$ppval, tot_permBH$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")
Version | Author | Date |
---|---|---|
cd3bdf8 | Briana Mittleman | 2018-09-26 |
plot(-log10(tot_permBH$bh), main="Total BH corrected pval")
abline(h=1,col="Red")
Version | Author | Date |
---|---|---|
cd3bdf8 | Briana Mittleman | 2018-09-26 |
I am going to look how many variants pass the 10% FDR.
tot_qtl_10= tot_permBH %>% filter(-log10(bh) > 1)
nrow(tot_qtl_10)
[1] 1468
This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.
tot_qtl_10uniq= tot_permBH %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
tot_qtl_10uniq
n_distinct(sid)
1 568
nuc_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", header=T, stringsAsFactors = F)
Check to quality of the tests:
plot(nuc_permBH$ppval, nuc_permBH$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")
Version | Author | Date |
---|---|---|
cd3bdf8 | Briana Mittleman | 2018-09-26 |
plot(-log10(nuc_permBH$bh), main="Nuclear BH corrected pval")
abline(h=1,col="Red")
Version | Author | Date |
---|---|---|
cd3bdf8 | Briana Mittleman | 2018-09-26 |
I am going to look how many variants pass the 10% FDR.
nuc_qtl_10= nuc_permBH %>% filter(-log10(bh) > 1)
nrow(nuc_qtl_10)
[1] 7025
This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.
nuc_qtl_10uniq= nuc_permBH %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
nuc_qtl_10uniq
n_distinct(sid)
1 2736
nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
x=tot_permBH %>% filter(bh < i ) %>% nrow()
nQTL_tot=c(nQTL_tot, x)
}
FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
x=nuc_permBH %>% filter(bh < i ) %>% nrow()
nQTL_nuc=c(nQTL_nuc, x)
}
nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")
ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")
Version | Author | Date |
---|---|---|
cd3bdf8 | Briana Mittleman | 2018-09-26 |
Look at distribution of SNP to peak in each fraction:
ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_histogram(binwidth=.15, alpha=.5 ) + geom_histogram(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.5,binwidth=.15) + annotate("text", x=1, y=950, col="Red", label="Total") + annotate("text", x=1, y=900, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=850, ymax=1000, color="Black", alpha=0)
Version | Author | Date |
---|---|---|
ea3cfeb | Briana Mittleman | 2018-09-26 |
ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_density( alpha=.25 ,fill="Black") + geom_density(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.25) + annotate("text", x=1, y=.77, col="Red", label="Total") + annotate("text", x=1, y=.72, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=.69, ymax=.8, color="Black", alpha=0)
Version | Author | Date |
---|---|---|
ea3cfeb | Briana Mittleman | 2018-09-26 |
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.1
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 workflowr_1.2.0 reshape2_1.4.3 forcats_0.3.0
[5] stringr_1.4.0 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
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.0 git2r_0.24.0 bindr_0.1.1 tools_3.5.1
[9] digest_0.6.17 lubridate_1.7.4 jsonlite_1.6 evaluate_0.13
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2
[17] rlang_0.2.2 cli_1.0.1 rstudioapi_0.9.0 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 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.4 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.11 modelr_0.1.2 magrittr_1.5
[37] whisker_0.3-2 backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[41] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.0
[49] crayon_1.3.4