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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Compare QTLs to those found with previous batch data

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.

Total

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)

Nuclear

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)

Extract genotypes:

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)

Total:

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:
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[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      
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[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