Last updated: 2019-04-29
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Knit directory: apaQTL/analysis/
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Rmd | 39a6572 | brimittleman | 2019-04-29 | add correlation genotype heatmap |
library(gdata)
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Attaching package: 'gdata'
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library(workflowr)
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library(gplots)
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library(tidyverse)
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I have about double the QTLs hear compared to before resequencing batch 4. I will look at the new QTL to see if there is evidence for them being false positives. I am going to see if there is structure in the genotypes for these QTLs.
The old QTLs are from the threeprimeseq repository.
Import old QTLs
oldtot=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].
OldTotQTLs= oldtot %>% filter(-log10(bh)>=1)
nrow(OldTotQTLs)
[1] 291
Import new QTLs:
newTotQTLs=read.table("../data/apaQTLs/Total_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newTotQTLs)
[1] 502
Filter out those matching from the old:
UniqueNewTot=newTotQTLs %>% semi_join(OldTotQTLs, by="sid")
There are only 105 new snps This makes sense because 1 sno associates with multiple peaks.
Write these out to fetch the genotypes:
write.table(UniqueNewTot, file="../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)
oldnuc=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].
OldNucQTLs= oldnuc %>% filter(-log10(bh)>=1)
nrow(OldNucQTLs)
[1] 615
Import new QTLs:
newNucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newNucQTLs)
[1] 1070
Filter out those matching from the old:
UniqueNewNuc=newNucQTLs %>% semi_join(OldNucQTLs, by="sid")
There are 200 new snps in this set.
write.table(UniqueNewNuc, file="../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)
I wrote a script to pull the doses from the vcf file. Run it with:
python extractGenotypes.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt
python extractGenotypes.py ../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt
I also need the header from the VCF to have the individuals:
head -n14 /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf | tail -n1 > ../data/QTLGenotypes/vcfheader.txt
#manually remove # and unneaded columns, keep snp and ind.
vcfhead=read.table("../data/QTLGenotypes/vcfheader.txt", header = T)
input sample list:
samples=read.table("../data/phenotype/SAMPLE.txt")
samplist=as.vector(samples$V1)
totgeno=read.table("../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
Correlation:
totgeneCorr=round(cor(totgeno),2)
heatmap.2(as.matrix(totgeneCorr),trace="none", dendrogram =c("none"), main="Genotype correlation\n for new Total QTL snps")
###Nuclear
nucgeno=read.table("../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
Correlation:
nucgeneCorr=round(cor(nucgeno),2)
heatmap.2(as.matrix(nucgeneCorr),trace="none", dendrogram =c("none"),main="Genotype correlation \n for new Nuclear QTL snps")
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.0
[9] tidyverse_1.2.1 gplots_3.0.1 workflowr_1.3.0 gdata_2.18.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 plyr_1.8.4
[4] compiler_3.5.1 pillar_1.3.1 git2r_0.23.0
[7] bitops_1.0-6 tools_3.5.1 digest_0.6.18
[10] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38
[16] pkgconfig_2.0.2 rlang_0.3.1 cli_1.0.1
[19] rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[22] withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 generics_0.0.2
[28] fs_1.2.6 gtools_3.8.1 caTools_1.17.1.1
[31] rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[34] glue_1.3.0 R6_2.3.0 readxl_1.1.0
[37] rmarkdown_1.10 modelr_0.1.2 magrittr_1.5
[40] whisker_0.3-2 backports_1.1.2 scales_1.0.0
[43] htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0
[46] colorspace_1.3-2 KernSmooth_2.23-15 stringi_1.2.4
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[52] crayon_1.3.4