Last updated: 2020-02-13

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
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    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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Rmd e5bd68f brimittleman 2020-02-13 add other cell nuclear enriched

To estimate the nuclear intronic reads in other cell types, I will get a set of our intronic Nuclear PAS and see if they are in other cell types. The intuition for this is for our nuclear enriched PAS with reads in total, these are nuclear but we see them in total mRNA because we have not seperated the nucleus out of the total.

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()
mkdir ../data/TissueData
sig=read.table("../data/DiffIso/TN_diff_isoform_AllChrom_cluster_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success")  %>% separate(cluster, into=c("chr","gene"),sep=":") 

sig$p.adjust=as.numeric(as.character(sig$p.adjust))
sig= sig %>% filter(p.adjust <=.05)

PAS=read.table("../data/peaks_5perc/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.SAF",stringsAsFactors = F,header = T) %>% separate(GeneID, into=c("num", "chr", "start", "end", "strand", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_") %>%  mutate(intron=paste("chr", Chr, ":", Start, ":", End, ":", gene,sep=""),strandFix=ifelse(strand=="+","-","+")) %>% select(num, loc, intron, strandFix)


ES=read.table("../data/DiffIso/EffectSizes.txt", header = T) %>% inner_join(PAS, by="intron")
Warning: Column `intron` joining factor and character vector, coercing into
character vector
ES_sig= ES %>% filter(abs(deltaPAU) > .2) %>% separate(intron,into=c("chr", "start","end","gene"),sep=":")%>% semi_join(sig,by="gene")

ES_nuclearIntron=ES_sig %>% filter(loc=="intron", deltaPAU <0)


PASnumbed=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed", col.names = c("chr", "start", "end","ID", "score", "strand"),stringsAsFactors = F) %>% separate(ID, into=c("pnum", "geneloc"),sep=":") %>% mutate(num=paste("peak", pnum,sep="")) %>% semi_join(ES_nuclearIntron,by="num") %>% mutate(ID=paste(num, geneloc, sep="_"),chrom=paste("chr", chr, sep="")) %>% select(chrom, start,end, ID, score, strand)


write.table(PASnumbed, file="../data/TissueData/NuclearEnriched.bed", col.names = F,row.names = F, quote=F, sep="\t")
nrow(ES_nuclearIntron)
[1] 387

There are 387 of these.

sort -k1,1 -k2,2n ../data/TissueData/NuclearEnriched.bed > ../data/TissueData/NuclearEnriched_sort.bed

Download human tissue PAS from https://polyasite.unibas.ch/samples.

Brain, Muscle, Kidney, Liver, from Derti et al 2012.

Testes, breast, stem cell, ovary from Lianoglou, S. et al 2013

I need to lift these from hg38 to hg19.

sbatch tissuePAS2hg19.sh
sbatch ClosestTissuePAS.sh

Brain.DistanceMyPAS2Anno.bed kidney.DistanceMyPAS2Anno.bed muscle.DistanceMyPAS2Anno.bed stemcell.DistanceMyPAS2Anno.bed breast.DistanceMyPAS2Anno.bed liver.DistanceMyPAS2Anno.bed ovary.DistanceMyPAS2Anno.bed testes.DistanceMyPAS2Anno.bed

filter out those with 0 in the sample of interest

resNames=c("chr1","start1","end1",'PAS', 'score', 'strand', 'chr2', 'start2', 'end2', 'anno', 'TPMsamp', 'st','dist')
Brainres=read.table("../data/TissueData/Brain.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="Brain")

kidneyres=read.table("../data/TissueData/kidney.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="kidney")

muscleres=read.table("../data/TissueData/muscle.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="muscle")


stemcellres=read.table("../data/TissueData/stemcell.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="stemcell")


muscleres=read.table("../data/TissueData/muscle.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="muscle")


breastres=read.table("../data/TissueData/breast.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="breast")


liverres=read.table("../data/TissueData/liver.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="liver")


ovaryres=read.table("../data/TissueData/ovary.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="ovary")

testesres=read.table("../data/TissueData/testes.DistanceMyPAS2Anno.bed",col.names = resNames) %>% filter(abs(dist) <10, TPMsamp>0) %>% mutate(Tissue="testes")


Alltissues=Brainres %>% 
  bind_rows(kidneyres) %>% 
  bind_rows(muscleres) %>% 
  bind_rows(breastres) %>% 
  bind_rows(liverres) %>% 
  bind_rows(ovaryres) %>%
  bind_rows(testesres) %>% 
  bind_rows(stemcellres)



AlltissueProp=Alltissues %>% group_by(Tissue) %>% summarise(nOverlap=n(), percOver=nOverlap/387)
ggplot(AlltissueProp,aes(x=Tissue,fill=Tissue, y=nOverlap)) + geom_bar(stat="identity") + labs(title="Number of Intronic Nuclear enriched (n=387) Used in Other Tissues", y="Number of PAS")  + scale_fill_brewer(palette = "Dark2")

ggplot(AlltissueProp,aes(x=Tissue,fill=Tissue, y=percOver)) + geom_bar(stat="identity")+ labs(title="Percent of Intronic Nuclear enriched Used in Other Tissues", y="Percent of PAS") + scale_fill_brewer(palette = "Dark2")


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] 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        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1