Last updated: 2020-01-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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File Version Author Date Message
Rmd 5c1ca36 brimittleman 2020-01-10 swtich to >/2
html 181523d brimittleman 2020-01-06 Build site.
Rmd 3bc05f3 brimittleman 2020-01-06 add empirical pvalues
html 99f3002 brimittleman 2020-01-06 Build site.
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  
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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_genes=read.table("../data/DiffIso_Nuclear/SignifianceEitherGENES_Nuclear.txt", stringsAsFactors = F, col.names = "genes")
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_genes=read.table("../data/DiffIso_Total/SignifianceEitherGENES_Total.txt", stringsAsFactors = F, col.names = "genes")
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 )

empirical pvalues

nuclearEx=Nuclear_100tests %>% filter(explainedOverlap>=nrow(explainedNuclear)) %>% nrow() / 1000
nuclearUnEx=Nuclear_100tests %>% filter(unexplainedOverlap>=nrow(unexplainedNuclear)) %>% nrow() / 1000

totalEx=Total_100tests %>% filter(explainedOverlap>=nrow(explainedTotal)) %>% nrow() / 1000
totalUnEx=Total_100tests %>% filter(unexplainedOverlap>=nrow(unexplainedTotal)) %>% nrow() / 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") + annotate("text", x = 200, y = 40, label = paste("EmpPvalue=", nuclearEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
181523d brimittleman 2020-01-06
99f3002 brimittleman 2020-01-06

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")+ annotate("text", x = 150, y = 40, label = paste("EmpPvalue=", nuclearUnEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
181523d brimittleman 2020-01-06
99f3002 brimittleman 2020-01-06

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")+ annotate("text", x = 150, y = 40, label = paste("EmpPvalue=", totalEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
181523d brimittleman 2020-01-06
99f3002 brimittleman 2020-01-06

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")+ annotate("text", x = 150, y = 30, label = paste("EmpPvalue=", totalEx), col="red")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
181523d brimittleman 2020-01-06

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")

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] workflowr_1.5.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4