Last updated: 2019-09-17
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/NuclearSpecIncludeNotTested.Rmd
Modified: analysis/PASdescriptiveplots.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/compareAnnotatedpas.Rmd
Modified: analysis/nucSpecinEQTLs.Rmd
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Modified: analysis/version15bpfilter.Rmd
Modified: code/DistPAS2Sig.py
Modified: code/apaQTLsnake.err
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
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | f8cb7b8 | brimittleman | 2019-09-17 | move inclusive, get numbers for paper |
html | 2bc4187 | brimittleman | 2019-09-15 | Build site. |
Rmd | 7c4debc | brimittleman | 2019-09-15 | add inclusive PAS analysis |
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(qvalue)
I want to call QTLs with all of the PAS to check for specificity.
I can filter the phenotypes in ../data/phenotype by the PAS in
APApeak_Phenotype_GeneLocAnno.Total.fc
allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed", header=F,col.names=c("chr","start", "end", "ID", "score","strand"),stringsAsFactors = F) %>% separate(ID, into = c("PASnum", "geneID"),sep=":") %>% mutate(PAS_ID=paste("peak", PASnum, sep=""))
TotalPheno:
totalPheo=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc",header = T,stringsAsFactors = F)
totalPheo_peaknum= totalPheo%>% separate(chrom, into=c("chr","start","end", "ID"),sep=":") %>% separate(ID, into=c("gene", "loc", "strand", "PAS_ID"), sep="_") %>% semi_join(allPAS, by="PAS_ID") %>% mutate(ID=paste(gene,loc,strand,PAS_ID,sep="_"), chrom= paste(chr,start,end,ID,sep=":")) %>% select(chrom)
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
totalPheo_filt=totalPheo %>% semi_join(totalPheo_peaknum,by="chrom")
NuclearPheno:
NuclearPheno=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.fc",header = T,stringsAsFactors = F)
NuclearPheno_peaknum= NuclearPheno%>% separate(chrom, into=c("chr","start","end", "ID"),sep=":") %>% separate(ID, into=c("gene", "loc", "strand", "PAS_ID"), sep="_") %>% semi_join(allPAS, by="PAS_ID") %>% mutate(ID=paste(gene,loc,strand,PAS_ID,sep="_"), chrom= paste(chr,start,end,ID,sep=":")) %>% select(chrom)
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
NuclearPheno_filt=NuclearPheno %>% semi_join(NuclearPheno,by="chrom")
write out:
mkdir ../data/phenotype_inclusivePAS
mkdir ../data/apaQTLNominal_inclusive
write.table(NuclearPheno_filt, row.names = F, col.names = T, "../data/phenotype_inclusivePAS/APApeak_Phenotype_GeneLocAnno.Nuclear.fc", quote=F)
write.table(totalPheo_filt, row.names = F, col.names = T, "../data/phenotype_inclusivePAS/APApeak_Phenotype_GeneLocAnno.Total.fc", quote=F)
#!/bin/bash
#python2
gzip APApeak_Phenotype_GeneLocAnno.Total.fc
gzip APApeak_Phenotype_GeneLocAnno.Nuclear.fc
python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Total.fc.gz
python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz
sh APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz_prepare.sh
sh APApeak_Phenotype_GeneLocAnno.Total.fc.gz_prepare.sh
head -n5 APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.4PCs
head -n5 APApeak_Phenotype_GeneLocAnno.Total.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.fc.gz.4PCs
#code dir
sbatch apaQTL_nominalInclusive.sh
#concatinate res:
cat ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_chr* > ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_allChr.txt
cat ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_chr* > ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_allChr.txt
mkdir ../data/QTLoverlap_inclusive/
python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.qqnorm_allChr.txt ../data/QTLoverlap_inclusive/TotalQTLinNuclearNominal_inc.txt
python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_inclusive/APApeak_Phenotype_GeneLocAnno.Total.fc.gz.qqnorm_allChr.txt ../data/QTLoverlap_inclusive/NuclearQTLinTotalNominal_inc.txt
totAPAinNuc=read.table("../data/QTLoverlap_inclusive/TotalQTLinNuclearNominal_inc.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_tot=pi0est(totAPAinNuc$pval, pi0.method = "bootstrap")
nucAPAinTot=read.table("../data/QTLoverlap_inclusive/NuclearQTLinTotalNominal_inc.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_nuc=pi0est(nucAPAinTot$pval, pi0.method = "bootstrap")
par(mfrow=c(1,2))
hist(totAPAinNuc$pval, xlab="Nuclear Pvalue", main="Significant Total APA QTLs \n Nuclear")
text(.8,300, paste("pi_1=", round((1-qval_tot$pi0), digit=3), sep=" "))
hist(nucAPAinTot$pval, xlab="Total Pvalue", main="Significant Nuclear APA QTLs \n Total")
text(.8,350, paste("pi_1=", round((1-qval_nuc$pi0), digit=3), sep=" "))
Version | Author | Date |
---|---|---|
2bc4187 | brimittleman | 2019-09-15 |
totAPAinNuc_notsig=totAPAinNuc %>% filter(pval>.05)
nrow(totAPAinNuc_notsig)
[1] 97
nucAPAinTot_notsig=nucAPAinTot %>% filter(pval>.05)
nrow(nucAPAinTot_notsig)
[1] 151
prop.test(x=c(97,151),n=c(443,603), alternative="less")
2-sample test for equality of proportions with continuity
correction
data: c(97, 151) out of c(443, 603)
X-squared = 1.2283, df = 1, p-value = 0.1339
alternative hypothesis: less
95 percent confidence interval:
-1.00000000 0.01394063
sample estimates:
prop 1 prop 2
0.2189616 0.2504146
location for specific:
totAPAinNuc_notsig_loc=totAPAinNuc_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% group_by(loc) %>% summarize(n=n())
totAPAinNuc_notsig_loc
# A tibble: 5 x 2
loc n
<chr> <int>
1 cds 11
2 end 6
3 intron 25
4 utr3 54
5 utr5 1
25/nrow(totAPAinNuc_notsig)
[1] 0.257732
nucAPAinTot_notsig_loc=nucAPAinTot_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% group_by(loc) %>% summarize(n=n())
nucAPAinTot_notsig_loc
# A tibble: 5 x 2
loc n
<chr> <int>
1 cds 7
2 end 22
3 intron 50
4 utr3 67
5 utr5 5
50/nrow(nucAPAinTot_notsig)
[1] 0.3311258
Look at nuclear specific in eQTL:
nucAPAinTot_notsig_small=nucAPAinTot_notsig %>% separate(peakID, into=c("chr","start","end", "pasid"), sep=":") %>% separate(pasid, into=c("gene","loc","strand","pas"), sep="_") %>% select(gene, pas, snp)
write.table(nucAPAinTot_notsig_small, "../data/QTLoverlap_inclusive/NuclearSpecApaQTLinclusive.txt", col.names=T, row.names=F, quote=F)
Test these is edata:
python nucspecinE.py
nucspecinE=read.table("../data/QTLoverlap_inclusive/NuclearSpecApaQTLinclusive_withE.txt", stringsAsFactors =F, col.names=c("peakID", 'snp','dist', 'pval', 'slope'))
sig=nucspecinE %>% filter(pval<.05)
nrow(sig)
[1] 13
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] qvalue_2.14.0 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] tidyselect_0.2.5 reshape2_1.4.3 splines_3.5.1 haven_1.1.2
[5] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[9] yaml_2.2.0 utf8_1.1.4 rlang_0.4.0 pillar_1.3.1
[13] glue_1.3.0 withr_2.1.2 modelr_0.1.2 readxl_1.1.0
[17] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0 workflowr_1.4.0
[21] cellranger_1.1.0 rvest_0.3.2 evaluate_0.12 knitr_1.20
[25] fansi_0.4.0 highr_0.7 broom_0.5.1 Rcpp_1.0.2
[29] scales_1.0.0 backports_1.1.2 jsonlite_1.6 fs_1.3.1
[33] hms_0.4.2 digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1 magrittr_1.5
[41] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[45] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[53] git2r_0.25.2 compiler_3.5.1