Last updated: 2019-06-12
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/DiffIsoAnalysis.Rmd
Modified: analysis/PASusageQC.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/choosePCs.Rmd
Modified: analysis/corrbetweenind.Rmd
Modified: analysis/exvunexpeQTL.Rmd
Modified: analysis/mapapaQTL.Rmd
Modified: analysis/motifDisruption.Rmd
Modified: analysis/nascenttranscription.Rmd
Modified: analysis/nucintronicanalysis.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/rna_netseq_h3k12ac.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
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Modified: code/apaQTLCorrectPvalMakeQQ.R
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Modified: code/apaQTLsnake.err
Modified: code/bam2bw.sh
Modified: code/bed2saf.py
Modified: code/cluster.json
Modified: code/config.yaml
Deleted: code/test.txt
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File | Version | Author | Date | Message |
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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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✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
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✖ dplyr::filter() masks stats::filter()
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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))
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))
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 |
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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 |
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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))
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))
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 |
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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 |
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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 |
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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 |
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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