Usage for nuclear specific apaQTLs

Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
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File	Version	Author	Date	Message
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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 1.4.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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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. 
-0.3167272 -0.0357638  0.0007393  0.0088791  0.0466066  0.4408295

summary(nucAPAinTot_effect_notsig$deltaPAU)

      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.2264930 -0.0499192  0.0002767  0.0067828  0.0563865  0.3702486

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_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        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] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

Usage for nuclear specific apaQTLs

Briana Mittleman

6/16/2019