ChromHMM enrichment for apaQTLs

Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Emain-1.pdf
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Esubset-1.pdf
    Deleted:    docs/figure/chromHHMQTL.Rmd/figure3D-1.pdf
    Deleted:    reads_graphs.Rmd

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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	ab8482d	brimittleman	2019-08-01	Build site.
Rmd	99c751e	brimittleman	2019-08-01	pdf for figure 3
html	b89e999	brimittleman	2019-06-13	Build site.
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)

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library(reshape2)


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    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
b89e999	brimittleman	2019-06-13
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
b89e999	brimittleman	2019-06-13
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
b89e999	brimittleman	2019-06-13
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
b89e999	brimittleman	2019-06-13
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
b89e999	brimittleman	2019-06-13
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
b89e999	brimittleman	2019-06-13
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,unexplainedResMPropQuant,explainedResMPropQuant)) 
AllQTLsProp95$category=factor(AllQTLsProp95$category, levels=c("NuclearApa", "UnexplainedeQTL","ExplainedeQTL"))

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(0.9)) + theme(axis.text.x=element_text(angle=45, hjust=1),legend.position = "top", legend.title = element_blank())+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for all QTLs") + scale_fill_brewer(palette="Dark2")

Version	Author	Date
ab8482d	brimittleman	2019-08-01
b89e999	brimittleman	2019-06-13
6aa908e	brimittleman	2019-06-12
38a75ec	brimittleman	2019-06-04

AllQTLsProp95_certcat=AllQTLsProp95 %>% filter(HMMname %in% c("Active_Promoter", "Strong_Enhancer","Txn_Elongation", "Weak_Enhancer", "Weak_Promter","Poised_Promoter", "Weak_Txn" ))

AllQTLsProp95_certcat$HMMname= factor(AllQTLsProp95_certcat$HMMname, levels=c('Strong_Enhancer','Weak_Enhancer','Poised_Promoter',"Active_Promoter", 'Weak_Promter','Txn_Elongation',  'Weak_Txn' ))


chromhmmplot=ggplot(AllQTLsProp95_certcat, 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(0.9)) + theme(axis.text.x=element_text(angle=45, hjust=1),legend.position = "top", legend.title = element_blank())+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for all QTLs") + scale_fill_brewer(palette="Dark2",labels=c("apaQTL","Unexaplined eQTL", "Explained eQTL"))+scale_x_discrete(labels= c("Strong Enhancer", "Weak Enhancer", "Poised Promoter", "Active Promoter", "Transcription Elongation", "Weak Transcription"))

chromhmmplot

Version	Author	Date
ab8482d	brimittleman	2019-08-01
b89e999	brimittleman	2019-06-13
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(0.9)) + theme(axis.text.x=element_text(angle=45, hjust=1),legend.position = "top", legend.title = element_blank())+labs(y="Proportion of QTLs", x="ChromHMM Category", title="ChromHMM locations for apaQTLs") +scale_fill_manual(values=c("deepskyblue3","darkviolet"))

Version	Author	Date
ab8482d	brimittleman	2019-08-01

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_1.4.0 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         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         compiler_3.5.1     pillar_1.3.1      
 [7] git2r_0.25.2       highr_0.7          tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4

ChromHMM enrichment for apaQTLs

Briana Mittleman

5/30/2019