ChromHMM enrichment for apaQTLs

Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
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    Deleted:    code/test.txt

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), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
html	6aa908e	brimittleman	2019-06-12	Build site.
Rmd	16b1696	brimittleman	2019-06-12	new genos
html	38a75ec	brimittleman	2019-06-04	Build site.
html	946ab10	brimittleman	2019-05-31	Build site.
Rmd	e790bfa	brimittleman	2019-05-31	add all res by tot apa
html	d30d453	brimittleman	2019-05-30	Build site.
Rmd	1321989	brimittleman	2019-05-30	add chromhmm analysis

library(tidyverse)

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

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library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

The goal of this analysis is to see if there is a difference in the QTL chrom hmm locations between the total and nuclear set. I will resample with replacement the number of times for then number of QTLs I have. Then I will count how many QTLs are in each chromHMM region. I will plot these values and include error bars.

I want one script that will do the replacement, mapping to chromHMM, and append to a final dataframe. This dataframe will have a row for each category and a column for each sample. I can do this using a dictionary with the category as the keys and a list of values representing the number of snps in each catgory.

I will use pybedtools to overlap the snps with the ChromHMM categories.

The categories are in: /project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed

First I need to make bedfiles for each of the QTL lists.

python QTL2bed.py Total

python QTL2bed.py Nuclear

mkdir ../data/HMMqtls
sbatch runHMMpermuteAPAqtls.sh

chromHmm=read.table("../data/HMMqtls/chromHMM_regions.txt", col.names = c("HMMcat", "HMMname"), stringsAsFactors = F)
nucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed", header = T)
totQTLs=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.bed", header = T)

TotalRes=read.table("../data/HMMqtls/TotalAPAqtls.HMM1000times.txt", stringsAsFactors = F, col.names =c("HMMcat", seq(1,1000)))

TotalResM=melt(TotalRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  group_by(HMMname) %>% summarise(Mean=mean(value), SD=sd(value)) %>% mutate(Category="Total_APA")

TotalResMProp=melt(TotalRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(totQTLs), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(Mean=mean(propQTL), SD=sd(propQTL)) %>% mutate(Category="Total_APA")

ggplot(TotalResM, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04
946ab10	brimittleman	2019-05-31

ggplot(TotalResMProp, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04
946ab10	brimittleman	2019-05-31

NuclearRes=read.table("../data/HMMqtls/NuclearAPAqtls.HMM1000times.txt", stringsAsFactors = F, col.names =c("HMMcat", seq(1,1000)))

NuclearResM=melt(NuclearRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  group_by(HMMname) %>% summarise(Mean=mean(value), SD=sd(value)) %>% mutate(Category="Nuclear_APA")

NuclearResMProp=melt(NuclearRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(nucQTLs), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(Mean=mean(propQTL), SD=sd(propQTL)) %>% mutate(Category="Nuclear_APA")

ggplot(NuclearResM, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

ggplot(NuclearResMProp, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

Next run this for eQTLs as well

Run for explained and unexplained eQTLs:

explained=read.table("../data/Li_eQTLs/explained_FDR10.SNPs.noChr.txt", col.names = c("SNPchr", "SNPend")) %>% mutate(SNPstart=as.integer(SNPend)-1, gene="NA", score=".", strand="+") %>% dplyr::select(SNPchr, SNPstart, SNPend, gene, score, strand)

write.table(explained, file="../data/Li_eQTLs/explained_FDR10.SNPs.noChr.bed", col.names = T,row.names = F, quote = F, sep="\t")


unexplained=read.table("../data/Li_eQTLs/unexplained_FDR10.SNPs.noChr.txt", col.names = c("SNPchr", "SNPend")) %>% mutate(SNPstart=as.integer(SNPend)-1, gene="NA", score=".", strand="+") %>% dplyr::select(SNPchr, SNPstart, SNPend, gene, score, strand)

write.table(unexplained, file="../data/Li_eQTLs/unexplained_FDR10.SNPs.noChr.bed", col.names = T,row.names = F, quote = F, sep="\t")

sbatch runHMMpermuteeQTLS.sh

Explained Res

explainedRes=read.table("../data/HMMqtls/explainedQTLs.HMM1000times.txt", stringsAsFactors = F, col.names =c("HMMcat", seq(1,1000)))


explainedResM=melt(explainedRes, id.vars="HMMcat") %>% inner_join(chromHmm, by="HMMcat") %>%  group_by(HMMname) %>% summarise(Mean=mean(value), SD=sd(value))  %>% mutate(Category="Explained_eQTL")

explainedResMProp=melt(explainedRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(explained), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(Mean=mean(propQTL), SD=sd(propQTL)) %>% mutate(Category="Explained_eQTL")

ggplot(explainedResM, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
38a75ec	brimittleman	2019-06-04
946ab10	brimittleman	2019-05-31

Unexplained Res

unexplainedRes=read.table("../data/HMMqtls/unexplainedeQTLs.HMM1000times.txt", stringsAsFactors = F, col.names =c("HMMcat", seq(1,1000)))

unexplainedResM=melt(unexplainedRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  group_by(HMMname) %>% summarise(Mean=mean(value), SD=sd(value)) %>% mutate(Category="Unexplained_eQTL")

unexplainedResMProp=melt(unexplainedRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(unexplained), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(Mean=mean(propQTL), SD=sd(propQTL)) %>% mutate(Category="Unexplained_eQTL")

ggplot(unexplainedResM, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5) + theme(axis.text.x=element_text(angle=90, hjust=1))

Version	Author	Date
38a75ec	brimittleman	2019-06-04
946ab10	brimittleman	2019-05-31

AllQTLs=as.data.frame(rbind(NuclearResM,TotalResM,explainedResM,unexplainedResM)) 

AllQTLsProp=as.data.frame(rbind(NuclearResMProp,TotalResMProp,explainedResMProp,unexplainedResMProp))

ggplot(AllQTLs, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity", position="dodge")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5, position = position_dodge(0.75)) + theme(axis.text.x=element_text(angle=90, hjust=1))+labs(y="Number of QTLs", x="ChromHMM Category", title="ChromHMM locations for all QTLs")

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

ggplot(AllQTLsProp, aes(x=HMMname, y=Mean, fill=Category)) + geom_bar(stat="identity", position="dodge")+ geom_errorbar(aes(ymin=Mean-SD, ymax=Mean+SD),width=.5, position = position_dodge(0.75)) + theme(axis.text.x=element_text(angle=90, hjust=1))+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for all QTLs")

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

Plot the 95% confidence interval:

unexplainedResMPropQuant=melt(unexplainedRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(unexplained), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(list(enframe(quantile(propQTL, probs=c(0.25,0.5,0.75))))) %>% unnest() %>% spread(name, value)%>% mutate(category="UnexplainedeQTL")
colnames(unexplainedResMPropQuant)=c("HMMname", "twentyfive", "fifty", "seventyfive", "category")

explainedResMPropQuant=melt(explainedRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(explained), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(list(enframe(quantile(propQTL, probs=c(0.25,0.5,0.75))))) %>% unnest() %>% spread(name, value)%>% mutate(category="ExplainedeQTL")
colnames(explainedResMPropQuant)=c("HMMname", "twentyfive", "fifty", "seventyfive", "category")


NuclearResMPropQuant=melt(NuclearRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(nucQTLs), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(list(enframe(quantile(propQTL, probs=c(0.25,0.5,0.75))))) %>% unnest() %>% spread(name, value)%>% mutate(category="NuclearApa")
colnames(NuclearResMPropQuant)=c("HMMname", "twentyfive", "fifty", "seventyfive", "category")


TotalResMPropQuant=melt(TotalRes, id.vars="HMMcat") %>%inner_join(chromHmm, by="HMMcat") %>%  mutate(nQTL= nrow(totQTLs), propQTL=value/nQTL)%>%  group_by(HMMname) %>% summarise(list(enframe(quantile(propQTL, probs=c(0.25,0.5,0.75))))) %>% unnest() %>% spread(name, value) %>% mutate(category="TotalApa")
colnames(TotalResMPropQuant)=c("HMMname", "twentyfive", "fifty", "seventyfive", "category")

AllQTLsProp95=as.data.frame(rbind(NuclearResMPropQuant,TotalResMPropQuant,unexplainedResMPropQuant,explainedResMPropQuant))

ggplot(AllQTLsProp95, aes(x=HMMname, y=fifty, fill=category)) + geom_bar(stat="identity", position="dodge")+ geom_errorbar(aes(ymin=twentyfive, ymax=seventyfive),width=.5, position = position_dodge(0.75)) + theme(axis.text.x=element_text(angle=90, hjust=1))+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for all QTLs")

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

Plot only the total and nuclear:

APAqtlsProp95=as.data.frame(rbind(NuclearResMPropQuant,TotalResMPropQuant)) 

ggplot(APAqtlsProp95, aes(x=HMMname, y=fifty, fill=category)) + geom_bar(stat="identity", position="dodge")+ geom_errorbar(aes(ymin=twentyfive, ymax=seventyfive),width=.5, position = position_dodge(0.75)) + theme(axis.text.x=element_text(angle=90, hjust=1))+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for apaQTLs") +scale_fill_manual(values=c("deepskyblue3","darkviolet"))

Version	Author	Date
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

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] reshape2_1.4.3  workflowr_1.3.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   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_0.5.1     
[49] crayon_1.3.4

ChromHMM enrichment for apaQTLs

Briana Mittleman

5/30/2019