Differential APA and eQTLs

Last updated: 2020-01-06

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/Nuclear_HvC.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

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
Rmd	b0dd3b5	brimittleman	2020-01-06	add erichment histograms
html	c633e63	brimittleman	2020-01-06	Build site.
Rmd	5e85704	brimittleman	2020-01-06	add overlap with eQTLs

library(tidyverse)

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

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
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✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

To start to catalog my differential apa gene, I want to evaluate how many of them have an eQTL. I will use the eQTLs from Li et al. I have the explained and unexplained eGenes.

I will put them in ../data/Li_eqtls

mkdir ../data/Li_eqtls

I want to make plots that have bars for explained eQTL, unexplained eQTL, not an eQTL for total differences and nuclear differences.

explained=read.table("../data/Li_eqtls/explainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)
unexplained=read.table("../data/Li_eqtls/UnexplainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)

Nuclear:

sigNuclear=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") 

sigNuclear$p.adjust=as.numeric(as.character(sigNuclear$p.adjust))

sigNuclear_genes=sigNuclear %>% filter(p.adjust<.05) %>% separate(cluster, into=c("chrom", "genes"), sep=":") %>% dplyr::select(genes) %>% unique()

explainedNuclear= sigNuclear_genes %>% inner_join(explained, by = "genes")
unexplainedNuclear=sigNuclear_genes %>% inner_join(unexplained, by="genes")
NeitherNuclear= sigNuclear_genes %>%  anti_join(explained, by="genes") %>% anti_join(unexplained, by="genes")

cat=c("Explained", "Unexaplained", "Neither")
num=c(nrow(explainedNuclear), nrow(unexplainedNuclear), nrow(NeitherNuclear))

Nuclearqtl=as.data.frame(cbind(cat,num))

Nuclearqtl$cat=factor(Nuclearqtl$cat, levels=c("Explained","Unexaplained", "Neither"), ordered=T)
Nuclearqtl$num=as.numeric(as.character(Nuclearqtl$num ))

ggplot(Nuclearqtl,aes(x=cat, y=num)) +geom_bar(stat="identity") + geom_text(aes(label=num), vjust=1.6, color="white", size=3.5) + labs(title="Differential APA in Nuclear fraction and human eQTL", y="Number of genes", x="Overlap")

Version	Author	Date
c633e63	brimittleman	2020-01-06

Make this plot with proportion:

NuclearqtlProp=Nuclearqtl %>% mutate(Prop=num/nrow(sigNuclear_genes))

ggplot(NuclearqtlProp,aes(x=cat, y=Prop)) +geom_bar(stat="identity") + geom_text(aes(label=round(Prop,digits = 3)), vjust=1.6, color="white", size=3.5) + labs(title="Differential APA in Nuclear fraction and human eQTL", y="Proportion of dAPA genes", x="Overlap")

Version	Author	Date
c633e63	brimittleman	2020-01-06

Total

sigTotal=read.table("../data/DiffIso_Total/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") 

sigTotal$p.adjust=as.numeric(as.character(sigTotal$p.adjust))

sigTotal_genes=sigTotal %>% filter(p.adjust<.05) %>% separate(cluster, into=c("chrom", "genes"), sep=":") %>% dplyr::select(genes) %>% unique()

explainedTotal= sigTotal_genes %>% inner_join(explained, by = "genes")
unexplainedTotal=sigTotal_genes %>% inner_join(unexplained, by="genes")
NeitherTotal= sigTotal_genes %>%  anti_join(explained, by="genes") %>% anti_join(unexplained, by="genes")

catTotal=c("Explained", "Unexaplained", "Neither")
numTotal=c(nrow(explainedTotal), nrow(unexplainedTotal), nrow(NeitherTotal))

Totalqtl=as.data.frame(cbind(catTotal,numTotal))

Totalqtl$catTotal=factor(Totalqtl$catTotal, levels=c("Explained","Unexaplained", "Neither"), ordered=T)
Totalqtl$numTotal=as.numeric(as.character(Totalqtl$numTotal ))

ggplot(Totalqtl,aes(x=catTotal, y=numTotal)) +geom_bar(stat="identity") + geom_text(aes(label=numTotal), vjust=1.6, color="white", size=3.5) + labs(title="Differential APA in Total fraction and human eQTL", y="Number of genes", x="Overlap")

Version	Author	Date
c633e63	brimittleman	2020-01-06

Make this plot with proportion:

TotalqtlProp=Totalqtl %>% mutate(Prop=num/nrow(sigTotal_genes))

ggplot(TotalqtlProp,aes(x=cat, y=Prop)) +geom_bar(stat="identity") + geom_text(aes(label=round(Prop,digits = 3)), vjust=1.6, color="white", size=3.5) + labs(title="Differential APA in Total fraction and human eQTL", y="Proportion of dAPA genes", x="Overlap")

Version	Author	Date
c633e63	brimittleman	2020-01-06

Test enrichment

To test if these values are enriched I need to write a function that randomly chooses the same number of genes and assess the same overlaps. I will use all of the genes that we tested for APA.

NuclearAPAtested=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chrom", "gene"), sep=":") %>% dplyr::select(gene)
NuclearAPAtested_genes=as.vector(NuclearAPAtested$gene)
TotalAPAtested=read.table("../data/DiffIso_Total/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chrom", "gene"), sep=":") %>% dplyr::select(gene)
TotalAPAtested_genes=as.vector(TotalAPAtested$gene)

I can run it 100 times to have error bars.

permuteGenes <- function(InputGenes, nGenes, nTests){
  #InputGenes=NuclearAPAtested_genes
  #nGenes=nrow(sigNuclear_genes)
  #nTests=100
  explained=read.table("../data/Li_eqtls/explainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)
  unexplained=read.table("../data/Li_eqtls/UnexplainedEgenes.txt",col.names = c("genes"),stringsAsFactors = F)
  explainedOverlap=c()
  unexplainedOverlap=c()
  neitherOverlap=c()
  for (n in 1:nTests){
    genesTest=sample(InputGenes, nGenes)
    overlapE=intersect(genesTest, explained$genes) 
    overlapUN=intersect(genesTest, unexplained$genes) 
    diffs <- Reduce(setdiff,list(A = genesTest, B = explained$genes,C = unexplained$genes))
    explainedOverlap= c(explainedOverlap, length(overlapE))
    unexplainedOverlap=c(unexplainedOverlap,length(overlapUN))
    neitherOverlap=c(neitherOverlap, length(diffs))
  }
  DF=as.data.frame(cbind(explainedOverlap,unexplainedOverlap,neitherOverlap))
  return(DF)
}

Perform permutations 1000 times

Nuclear_100tests=permuteGenes(NuclearAPAtested_genes, nrow(sigNuclear_genes),1000 )

Total_100tests=permuteGenes(TotalAPAtested_genes, nrow(sigTotal_genes),1000 )

Plot as histograms

Nuclear Explained

ggplot(Nuclear_100tests,aes(x=explainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(explainedNuclear), col="red" )+labs(x="Number of Overlaps", title="Nuclear dAPA overlap with explained eGenes")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Nuclear Unexplained

ggplot(Nuclear_100tests,aes(x=unexplainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(unexplainedNuclear), col="red" ) +labs(x="Number of Overlaps", title="Nuclear dAPA overlap with unexplained eGenes")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Total Explained

ggplot(Total_100tests,aes(x=explainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(explainedTotal), col="red" )+labs(x="Number of Overlaps", title="Total dAPA overlap with explained eGenes")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Total Unexplained

ggplot(Total_100tests,aes(x=unexplainedOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =nrow(unexplainedTotal), col="red" ) +labs(x="Number of Overlaps", title="Total dAPA overlap with unexplained eGenes")

Warning: Ignoring unknown parameters: binwidth, bins, pad

###extra code:

# NuclearEx_mean=mean(Nuclear_100tests$explainedOverlap)
# NuclearEx_sd=sd(Nuclear_100tests$explainedOverlap)
# NuclearUn_mean=mean(Nuclear_100tests$unexplainedOverlap)
# NuclearUn_sd=sd(Nuclear_100tests$unexplainedOverlap)
# NuclearN_mean=mean(Nuclear_100tests$neitherOverlap)
# NuclearN_sd=sd(Nuclear_100tests$neitherOverlap)
# 
# Nuclear_100testsDf=as.data.frame(cbind(cat=c("Explained","Unexplained", "Neither"), Mean=c(NuclearEx_mean,NuclearUn_mean,NuclearN_mean), SD=c(NuclearEx_sd,NuclearUn_sd,NuclearN_sd), actual=Nuclearqtl$num))
# 
# 
# Nuclear_100testsDf$cat=factor(Nuclear_100testsDf$cat, levels=c("Explained","Unexplained", "Neither"), ordered=T)
# Nuclear_100testsDf$Mean=as.numeric(as.character(Nuclear_100testsDf$Mean))
# Nuclear_100testsDf$SD=as.numeric(as.character(Nuclear_100testsDf$SD))
# Nuclear_100testsDf$actual=as.numeric(as.character(Nuclear_100testsDf$actual))
# 
# ggplot(Nuclear_100testsDf,aes(x=cat,y=Mean)) + geom_bar(stat = "identity", alpha=.5) + geom_errorbar(aes(x=cat, ymin=Mean-SD, ymax=Mean+SD), width=0.2, size=1) + geom_point(aes(x=cat, y=actual), col="red")