Chromosomes for dAPA

Last updated: 2020-12-11

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	5d2905d	brimittleman	2020-12-11	add chrom analysis

In this analysis I will ask if there is a specific genomic distribution for differentially used PAS. I will do this both with all PAS then with only 1 per gene because I do not want to inflate the analysis for genes that have many PAS.

library(workflowr)

This is workflowr version 1.6.2
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──

✓ ggplot2 3.2.1     ✓ purrr   0.3.4
✓ tibble  2.1.3     ✓ dplyr   0.8.3
✓ tidyr   1.1.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.4.0

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

All PAS

I will work off the PAS that I was able to test.

#PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F)
PAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F)
dAPA=PAS %>% filter(SigPAU2=="Yes")

ggplot(dAPA, aes(x=chr,fill=chr)) + geom_bar(stat="count")

I need to normalize by then number of PAS on each chr:

PAS_num=PAS %>% group_by(chr) %>% summarise(nChr=n())

dAPA_num=dAPA %>% group_by(chr) %>% summarise(SigN=n())

PAS_dAPA_num= PAS_num %>% inner_join(dAPA_num, by="chr") %>% mutate(PropSig=SigN/nChr)


ggplot(PAS_dAPA_num, aes(x=chr,fill=chr, y=PropSig)) + geom_bar(stat="identity") +theme_classic()+ theme(axis.text.x = element_text(angle = 90))

Ask about significance:

Chroms=PAS_num$chr

enrichdAPA=c()
pvaldAPA=c()
pvalDepdAPA=c()
actualAPA=c()
expectdAPA=c()

for (i in Chroms){
  x=nrow(PAS %>% filter(SigPAU2=="Yes", chr==i))
  m=nrow(PAS %>% filter(chr==i))
  n=nrow(PAS %>% filter(chr!=i))
  k=nrow(PAS %>% filter(SigPAU2=="Yes"))
  N=nrow(PAS)
  actualAPA=c(actualAPA,x)
  exp=k*(m/N)
  expectdAPA=c(expectdAPA, exp)
  pvaldAPA=c(pvaldAPA, phyper(x-1,m,n,k,lower.tail=F))
  pvalDepdAPA=c(pvalDepdAPA, phyper(x,m,n,k,lower.tail=T))
  enrichval=(x/k)/(m/N)
  enrichdAPA=c(enrichdAPA,enrichval)
}

EnrichmentChrom=as.data.frame(cbind(Chroms, Actual=actualAPA, Expected=expectdAPA, Enrichment=enrichdAPA, Pval=enrichdAPA, DepP=pvalDepdAPA))

EnrichmentChrom$Enrichment=as.numeric(as.character(EnrichmentChrom$Enrichment))

ggplot(EnrichmentChrom,aes(x=Chroms, y=Enrichment,fill=Chroms)) + geom_bar(stat="identity")+ theme_classic()+theme(axis.text.x = element_text(angle = 90), legend.position = "none") + geom_hline(yintercept = 1)

Depletion on chr 3 and 4.

1 per gene

PAS_1per= PAS %>% group_by(gene, SigPAU2,chr) %>% summarise(nPer=n())
PAS_1per_sig= PAS_1per %>% filter(SigPAU2=="Yes")
PAS_1per_Nosig= PAS_1per %>% filter(SigPAU2=="No")

PAS_1perparse=PAS_1per_sig %>% rbind(PAS_1per_Nosig)


PAS_1perparse_chr=PAS_1perparse %>% group_by(chr,SigPAU2) %>% summarise(nPAS=n()) %>% spread(SigPAU2,nPAS) %>% mutate(PropSig=Yes/(Yes+No))

ggplot(PAS_1perparse_chr, aes(x=chr,fill=chr, y=PropSig)) + geom_bar(stat="identity") +theme_classic()+ theme(axis.text.x = element_text(angle = 90), legend.position = "none")

Significance:

PAS_1perparse_chr_g= PAS_1perparse_chr %>% select(-PropSig) %>% gather(key="Sig", value="nPAS",-chr) %>% ungroup()

enrichdAPA_gene=c()
pvaldAPA_gene=c()
pvalDepdAPA_gene=c()
actualAPA_gene=c()
expectdAPA_gene=c()

for (i in Chroms){
  x=PAS_1perparse_chr_g %>% filter(Sig=="Yes", chr==i) %>% summarise(n=sum(nPAS))
  x=x$n[1]
  m=PAS_1perparse_chr_g %>% filter(chr==i)%>% summarise(n=sum(nPAS))
  m=m$n[1]
  n=PAS_1perparse_chr_g %>% filter(chr!=i)%>% summarise(n=sum(nPAS))
  n=n$n[1]
  k=PAS_1perparse_chr_g %>% filter(Sig=="Yes")%>% summarise(n=sum(nPAS))
  k=k$n[1]
  N=sum(PAS_1perparse_chr_g$nPAS)
  actualAPA_gene=c(actualAPA_gene,x)
  exp=k*(m/N)
  expectdAPA_gene=c(expectdAPA_gene, exp)
  pvaldAPA_gene=c(pvaldAPA_gene, phyper(x-1,m,n,k,lower.tail=F))
  pvalDepdAPA_gene=c(pvalDepdAPA_gene, phyper(x,m,n,k,lower.tail=T))
  enrichval=(x/k)/(m/N)
  enrichdAPA_gene=c(enrichdAPA_gene,enrichval)
}

EnrichmentChromGene=as.data.frame(cbind(Chroms, Actual=actualAPA_gene, Expected=expectdAPA_gene, Enrichment=enrichdAPA_gene, Pval=pvaldAPA_gene, DepP=pvalDepdAPA_gene))

EnrichmentChromGene$Enrichment=as.numeric(as.character(EnrichmentChromGene$Enrichment))

ggplot(EnrichmentChromGene,aes(x=Chroms, y=Enrichment,fill=Chroms)) + geom_bar(stat="identity")+ theme_classic()+theme(axis.text.x = element_text(angle = 90), legend.position = "none") + geom_hline(yintercept = 1)

No chromosome is enriched or depleted for the numer of genes with significant PAS.

sessionInfo()

R version 3.6.1 (2019-07-05)
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] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.3     purrr_0.3.4    
 [5] readr_1.3.1     tidyr_1.1.0     tibble_2.1.3    ggplot2_3.2.1  
 [9] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.8         haven_2.3.1      lattice_0.20-38 
 [5] colorspace_1.4-1 vctrs_0.3.0      generics_0.0.2   htmltools_0.3.6 
 [9] yaml_2.2.0       rlang_0.4.6      later_0.8.0      pillar_1.4.2    
[13] withr_2.1.2      glue_1.3.1       DBI_1.1.0        dbplyr_1.4.2    
[17] modelr_0.1.8     readxl_1.3.1     lifecycle_0.1.0  munsell_0.5.0   
[21] gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5      evaluate_0.14   
[25] labeling_0.3     knitr_1.23       httpuv_1.5.1     fansi_0.4.0     
[29] broom_0.5.2      Rcpp_1.0.5       promises_1.0.1   backports_1.1.4 
[33] scales_1.1.0     jsonlite_1.6     farver_2.0.1     fs_1.3.1        
[37] hms_0.5.3        digest_0.6.20    stringi_1.4.3    grid_3.6.1      
[41] rprojroot_1.3-2  cli_2.2.0        tools_3.6.1      magrittr_1.5    
[45] lazyeval_0.2.2   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[49] ellipsis_0.2.0.1 xml2_1.3.2       reprex_0.3.0     lubridate_1.7.4 
[53] rstudioapi_0.10  assertthat_0.2.1 rmarkdown_1.13   httr_1.4.1      
[57] R6_2.4.0         nlme_3.1-140     git2r_0.26.1     compiler_3.6.1

Chromosomes for dAPA

Briana Mittleman

12/11/2020

All PAS

1 per gene