APA QTL example Heatmap

Last updated: 2019-04-02

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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/HistoneModandPAS.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/NuclearSpecQTL.Rmd
    Modified:   analysis/PeakToXper.Rmd
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    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/initialPacBioQuant.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    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:   analysis/unexplainedeQTL_analysis.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
Rmd	c4062e5	Briana Mittleman	2019-04-02	add general script
html	5244c0f	Briana Mittleman	2019-03-26	Build site.
Rmd	01af963	Briana Mittleman	2019-03-26	add example heatmap code

library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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✔ readr   1.3.1       ✔ forcats 0.4.0

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library(reshape2)


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library(cowplot)

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Attaching package: 'cowplot'

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    ggsave

Start with EIF2a example:

3 150302009 150302010 peak114357:EIF2A 5.39078186842105e-07 +

Get the phenotype

less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz | grep EIF2A_ > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/EIF2a_TotalPeaksPheno.txt

less chr3.dose.filt.vcf.gz | grep 3:150302010 > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/EIF2a_TotalPeaksGenotype.txt

less chr3.dose.filt.vcf.gz | head -n14 | tail -n1 > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/genotypeHeader.txt

phenohead=read.table("../data/ExampleQTLplot2/Phenotypeheader.txt", header = T,stringsAsFactors = F)
phenoEIF=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksPheno.txt", col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(phenoEIF, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/") 

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)

I want to join the genotype.

genoHead=read.table("../data/ExampleQTLplot2/genotypeHeader.txt", header = T,stringsAsFactors = F)
genoEIF=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksGenotype.txt", col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 

lettersGeno=read.table("../data/ExampleQTLplot2/EIF2a_TotalPeaksGenotype.txt", col.names =colnames(genoHead),stringsAsFactors = F, colClasses = c("character") ) %>% select(REF, ALT)

refAllele=as.character(lettersGeno$REF)
altAllele=as.character(lettersGeno$ALT)

genoMelt=melt(genoEIF, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)

Join these:

PhenandGene= meltpheno %>% inner_join(genoMelt, by="Individual")  %>% group_by(chrom, genotype) %>% summarise(SumNum=sum(num), SumDenom=sum(denom)) %>% mutate(PAU=SumNum/SumDenom) %>% separate(chrom, into=c("chr","start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_")

Check sums

Groupsumscheck = PhenandGene %>% group_by(genotype) %>% summarise(SUM=sum(PAU))

my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))



eif2aplot=ggplot(PhenandGene, aes(peak, genotype)) + geom_tile(aes(fill = PAU))+ scale_fill_gradientn(colors =my_palette(100)) + labs(title="EIF2A", y="Genotype",x="PAS")

eif2aplot

Version	Author	Date
5244c0f	Briana Mittleman	2019-03-26

ggsave(eif2aplot, file="../output/plots/testEIF2A.png")

Saving 7 x 5 in image

Make plot reproducible on midway

I want the script to take a fraction, gene, chr, a snp (chr:loc)

steps: * get phenotypes from /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz and /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz * get genotype from /project2/gilad/briana/YRI_geno_hg19/chrX.dose.filt.vcf.gz *rscript for making plot

I will write the rscript first:

makeQTLheatmap.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-G", "--geno"), action="store", default=NA, type='character',
              help="input genotype"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


phenohead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/Phenotypeheader.txt", header = T,stringsAsFactors = F)
pheno=read.table(opt$pheno, col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(pheno, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/") 

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)


genoHead=read.table("/project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/genotypeHeader.txt", header = T,stringsAsFactors = F)
geno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 


lettersGeno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F,colClasses = c("character")) %>% select(REF, ALT)

refAllele=lettersGeno$REF
altAllele=lettersGeno$ALT


genoMelt=melt(geno, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)

PhenandGene= meltpheno %>% inner_join(genoMelt, by="Individual")  %>% group_by(chrom, genotype) %>% summarise(SumNum=sum(num), SumDenom=sum(denom)) %>% mutate(PAU=SumNum/SumDenom) %>% separate(chrom, into=c("chr","start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_")



my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))



heatplot=ggplot(PhenandGene, aes(peak, genotype)) + geom_tile(aes(fill = PAU))+ scale_fill_gradientn(colors =my_palette(100)) + labs(title=opt$gene, y="Genotype" , x= "PAS")



ggsave(heatplot, filename=opt$output, device=png, height=10, width=10)

qtlHeatmap.sh

#!/bin/bash

#SBATCH --job-name=qtlHeatmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=qtlHeatmap.out
#SBATCH --error=qtlHeatmap.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Fraction=$1
gene=$2
chrom=$3
snp=$4


less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.${Fraction}.fixed.pheno_5perc.fc.gz | grep ${gene}_ > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt


less /project2/gilad/briana/YRI_geno_hg19/chr${chrom}.dose.filt.vcf.gz | grep ${snp} > /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt


Rscript makeQTLheatmap.R -P /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksPheno.txt -G  /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}PeaksGenotype.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/ExampleQTLPlots2/${gene}_${Fraction}_${SNP}.png

totalQTLs=read.table("../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", header=F)

14:65401627:65401711:CHURC1_-_peak48994

14:65389250

sbatch qtlHeatmap.sh "Total" "CHURC1" "14" "14:65389250"

12:57489617:57489715:STAT6_+_peak36983 12:57489648

sbatch qtlHeatmap.sh "Total" "STAT6" "12" "12:57489648"

Try a nuclear one:

nucQTLs=read.table("../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", header=F)

19:4688114:4688228:DPP9_+_peak77244

19:4680128

sbatch qtlHeatmap.sh "Nuclear" "DPP9" "19" "19:4680128"

4:83355978:83356052:ENOPH1_-_peak121076 4:83352186

sbatch qtlHeatmap.sh "Nuclear" "ENOPH1" "4" "4:83352186"

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] cowplot_0.9.4   reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.0.1   purrr_0.3.1     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.0.1    ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     workflowr_1.2.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.3.1      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_2.1.0      xfun_0.5         withr_2.1.2      xml2_1.2.0      
[25] httr_1.4.0       knitr_1.21       hms_0.4.2        generics_0.0.2  
[29] fs_1.2.6         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.4.0         readxl_1.3.0     rmarkdown_1.11  
[37] modelr_0.1.4     magrittr_1.5     whisker_0.3-2    backports_1.1.3 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.4-0 labeling_0.3     stringi_1.3.1    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4

APA QTL example Heatmap

Briana Mittleman

3/26/2019

Make plot reproducible on midway