Last updated: 2020-01-30

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Rmd 97dec83 brimittleman 2020-01-30 add decay and stability, nmd analysis 
library(workflowr)
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In this analysis I want to look as both decay and stability elements. I can see if there are overlaps with apaQTLs of the differentially used between total and nuclear.

NMD

Colombo et al. looked at transcriptome wide identification of NMD-targeted transcripts, in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5238794/pdf/189.pdf.

Supplemental table 2 has a list of differentailly expressed genes. They say top 1000 are the most significant. This analysis is in hela cells. The meta_meta column has the pvalues used for the final analysis. It is from a combind score from the SMGs and UPF1 data.

Nguyen et al. - Similar analysis in LCLs but from individuals with intelectual disability. The knock down experiment is in hela cells. table 1 has similar studies that used micro arrays

I will use the Colombo set because it is the most recent and comprehensive. This study used RNA seq rather than arrays.

mkdir ../data/NMD

Saved the supplementary table there.

Pull in the apaQTL genes, NMD genes, and Total/nuclear genes.

I will need all of those tested in each set to do the overlap.

NMD=read.table("../data/NMD/NMD_res_Colomboetal.txt",stringsAsFactors = F, header = T) 
NMD_sig= NMD %>% slice(1:1000)
apaTested=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = c('gene'),stringsAsFactors = F)
apaSig=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F) 

totalTest=read.table("../data/apaQTLs/TestedTotalapaQTLGenes.txt",col.names = c('gene'),stringsAsFactors = F)
totalSig=read.table("../data/apaQTLs/TotalapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F) 
#chr10:27035787:27035907:ABI1  
TvNTested=read.table("../data/DiffIso/EffectSizes.txt", header = T,stringsAsFactors = F) %>% separate(intron, into = c("chr", "start","end", "gene"),sep=":")

TvNsig=read.table("../data/highdiffsiggenes.txt",col.names = "gene", stringsAsFactors = F)

Overlap:

Nuclear QTL set

x=length(intersect(apaSig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(apaSig)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 17
#actual:
x
[1] 38
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 5.109008e-06

Total qtls:

x=length(intersect(totalSig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(totalSig)
  
#length(intersect(apaSig$gene, NMD$gene_name))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 10
#actual:
x
[1] 23
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.0001202593

Nuclear apaQTLs are more enriched for NMD

TVN set

x=length(intersect(TvNsig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=length(TvNsig$gene)
  
  #length(intersect(TvNsig$gene, NMD$gene_name))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 39
#actual:
x
[1] 64
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 4.589876e-05

Look at the expression independent ones:

expInd=read.table("../data/ExpressionIndependentapaQTLs.txt", header = T, stringsAsFactors = F) %>% dplyr::select(Gene) %>% unique()

x=length(intersect(expInd$Gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=length(expInd$Gene)
  


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 2
#actual:
x
[1] 0
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.5093779

No overlap here

Stability

From the ARED come back to this.


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_1.5.0

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     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4