Last updated: 2020-07-02

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Knit directory: Comparative_APA/analysis/

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Rmd 117fd63 brimittleman 2020-01-21 redo differential analysis with double filt

I want to ask if dAPA genes are enriched in the genes I found to have apaQTLs in my previous paper. This is similar to the analysis where I asked if dAPA genes are enriched for egenes. This is a more relevent analysis. I am asking if genes with species level differenences are enriched for those found to be genetically regulated.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
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
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

First I need to get the apaQTL genes from my other project. These are the nuclear apaQTLs at 5% fdr.

mkdir ../data/apaQTLGenes
#NuclearapaQTLGenes.txt
QTL_genes=read.table("../data/apaQTLGenes/NuclearapaQTLGenes.txt", header = F, stringsAsFactors = F, col.names = "genes")

Now I will pull in the genes from the dAPA analysis in the nuclear fraction. I need both the sigificant genes as well as all of those tested.

sigNuclear_genes=read.table("../data/DiffIso_Nuclear_DF//SignifianceEitherGENES_Nuclear.txt", stringsAsFactors = F, col.names = "genes")

NuclearAPAtested=read.table("../data/DiffIso_Nuclear_DF//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)

First look at the overlap.

actual=QTL_genes %>% inner_join(sigNuclear_genes,by="genes") %>% nrow()

I will use permutations selecting genes from all of those tested to see if this is significant.

permuteGenes <- function(InputGenes, nGenes, nTests){
  #InputGenes=NuclearAPAtested_genes
  #nGenes=nrow(sigNuclear_genes)
  #nTests=10000
  qtls=QTL_genes
  qtlOverlap=c()
  for (n in 1:nTests){
    genesTest=sample(InputGenes, nGenes)
    overlapqtl=intersect(genesTest, qtls$genes)
    qtlOverlap= c(qtlOverlap, length(overlapqtl))
  }
  DF=as.data.frame(cbind(qtlOverlap))
  return(DF)
}

Run this:

overlapSample=permuteGenes(NuclearAPAtested_genes,nrow(sigNuclear_genes), 10000)

an empirical pvalue is the number of sample overlaps greater than the actual overlap.

empricalpval=overlapSample %>% filter(qtlOverlap>=actual) %>% nrow() / 10000

Plot the results:

qtlover=ggplot(overlapSample,aes(x=qtlOverlap)) + geom_histogram(stat="count") + geom_vline(xintercept =actual, col="red" ) +labs(x="Number of Overlaps", title="Nuclear dAPA overlap with apaQTL Genes") +  annotate("text", x = 120, y = 300, label = paste("Empirical Pvalue=", empricalpval), col="black") +  annotate("text", x = 130, y = 400, label = "Actual Overlap", col="red") +theme_classic() + theme(axis.text.x = element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
Warning: Ignoring unknown parameters: binwidth, bins, pad
qtlover

Version Author Date
8f72fb7 brimittleman 2020-04-10
c59a7de brimittleman 2020-04-05
5800231 brimittleman 2020-01-21

supplemental:

pdf("../output/supplement/Fig2_figSup3.pdf", height=6, width=6)
qtlover
dev.off()
png 
  2 
overlapgenes=QTL_genes %>% inner_join(sigNuclear_genes,by="genes")

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6     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