Usage for nuclear specific apaQTLs

Last updated: 2019-06-17

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Knit directory: apaQTL/analysis/

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File	Version	Author	Date	Message
html	44d1fcc	brimittleman	2019-06-16	Build site.
Rmd	a5430c3	brimittleman	2019-06-16	add qtl and usage analysis

I am interested in which PAS are associated with the nuclear specific apaQTLs I found in the Nuclear Specific apaQTL analysis. In this analysis I found these QTL are more likely to overlap with eQTLs. I am interested in knowing if these PAS are used more in nuclear than total.

library(workflowr)

This is workflowr version 1.3.0
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()

nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% separate(peakID,into=c("chr", "start", "end", "geneID"),sep=":" ) %>% separate(geneID, into=c("gene", "loc", "strand", "peakNum"), sep="_")
nucAPAinTot_NOTSIG=nucAPAinTot %>% filter(pval>.05)
nucAPAinTot_SIG=nucAPAinTot %>% filter(pval<.05)
DiffIsoEffect=read.table("../data/DiffIso/EffectSizes.txt", stringsAsFactors = F, header = T) %>% separate(intron ,into=c("chr", "start", "end", "gene"), sep=":")

Join these:

nucAPAinTot_effect=nucAPAinTot %>% inner_join(DiffIsoEffect,by=c("start", "end", "gene")) %>% mutate(sig=ifelse(pval>.05, "No", "Yes"))

Plot these

ggplot(nucAPAinTot_effect,aes(x=sig, y=deltaPAU))+ geom_violin()

Version	Author	Date
44d1fcc	brimittleman	2019-06-16

ggplot(nucAPAinTot_effect,aes(group=sig, fill=sig,x=deltaPAU))+ geom_density(alpha=.5)

Version	Author	Date
44d1fcc	brimittleman	2019-06-16

nucAPAinTot_effect_sig=nucAPAinTot_effect %>% filter(sig=="Yes")
nucAPAinTot_effect_notsig=nucAPAinTot_effect %>% filter(sig=="No")


summary(nucAPAinTot_effect_sig$deltaPAU)

      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.3099072 -0.0425707 -0.0006168  0.0073859  0.0505083  0.4393265

summary(nucAPAinTot_effect_notsig$deltaPAU)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-0.337419 -0.058868 -0.013344  0.005798  0.051620  0.366965

Does not look like there is a big difference in diff iso effect size for nuclear specific vs overlapping apaQTLs.

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_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4

Usage for nuclear specific apaQTLs

Briana Mittleman

6/16/2019