Last updated: 2019-06-12

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Knit directory: apaQTL/analysis/

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Rmd 16b1696 brimittleman 2019-06-12 new genos
html 38a75ec brimittleman 2019-06-04 Build site.
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Rmd e790bfa brimittleman 2019-05-31 add all res by tot apa
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Rmd 1321989 brimittleman 2019-05-30 add chromhmm analysis

library(tidyverse)
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library(workflowr)
This is workflowr version 1.3.0
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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
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
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
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
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
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
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
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
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