Last updated: 2020-02-11

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
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    Modified:   code/DistPAS2Sig.py
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    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.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.

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Rmd 66100b8 brimittleman 2020-02-11 add tot v nuc effect size info and mech

In this analysis I will compare total nuclear effect sizes. I will see if there are a subset of QTLs identified in both fractions but whose effect size decreases in total. This could be do to buffering between nuclear and total.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract

This is looking at QTLs identified in both at 10% FDR.

TotQTL=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", header = T, stringsAsFactors = F) %>% select(Gene, Peak, sid, slope) %>% rename("TotalEffect"=slope)
NucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", header = T, stringsAsFactors = F) %>% select(Gene, Peak, sid, slope) %>% rename("NuclearEffect"=slope)

Find those with the same snp PAS pair:

BothQTL=TotQTL %>% inner_join(NucQTL, by=c("Gene", "Peak", 'sid')) %>% mutate(AbsTot=abs(TotalEffect), AbsNuc=abs(NuclearEffect))
BothQTL_abs= BothQTL %>% select(Peak,sid, Gene,contains("Abs"))
BothQTLGat= BothQTL_abs %>% gather("Fraction","AbsEffect", -Peak,-sid,-Gene)

I can take the absolute value because they are all in the same direction.

ggplot(BothQTLGat,aes(x=Fraction,y=AbsEffect)) +geom_boxplot() + stat_compare_means()

BothQTL_abs= BothQTL_abs %>% mutate(Diff=AbsNuc-AbsTot)

BothQTL_abs %>% filter(Diff<0) %>%  nrow()
[1] 57
BothQTL_abs %>% filter(Diff>0) %>% nrow()
[1] 47

I am interested in when the nuclear is larger than the total. There are 57 of these. Not significantly more.

look at these genes.

Buff=BothQTL_abs %>% filter(Diff<0) %>% select(Gene) %>% unique()

Opp=BothQTL_abs %>% filter(Diff>0) %>% select(Gene) %>% unique()
ggplot(BothQTL_abs,aes(x=AbsTot, y=AbsNuc))+ geom_point()

Are these genes enriched for NMD.

NMD=read.table("../data/NMD/NMD_res_Colomboetal.txt",stringsAsFactors = F, header = T) 
NMD_sig= NMD %>% dplyr::slice(1:1000)
x=length(intersect(Buff$Gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(Buff)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 1
#actual:
x
[1] 5
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.001678382

Compare to opposite:

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

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 1
#actual:
x
[1] 2
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.1438708

Opposite are not. Numbers are low but this could be interesting.

Compare the NMD fdrs for each.

NMD_metameta= NMD %>% select(gene_name,meta_meta) %>% rename("Gene"=gene_name) %>% inner_join(BothQTL_abs,by="Gene") %>% mutate(Buffer=ifelse(Diff <0, "Yes","No"))

ggplot(NMD_metameta,aes(x=Buffer, y=meta_meta,fill=Buffer))+ geom_violin() + stat_compare_means() + labs(y="NMD FDR") + scale_fill_brewer(palette = "Dark2")

ggplot(NMD_metameta,aes(fill=Buffer,by=Buffer, x=meta_meta))+ geom_density(alpha=.4)  + labs(x="NMD FDR")+ scale_fill_brewer(palette = "Dark2")

This means the genes with a decrease in effect size are more likely to be genes marked for NMD. This could signal NMD could be part of the reason we see a decrease in effect size.


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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1