Last updated: 2019-04-30

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File Version Author Date Message
Rmd f9b8195 brimittleman 2019-04-30 understand usage of new pas 
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
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── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started

These results have 30k more PAS than the previous runs. I also see a confusing shift in mean usage for all of the PAS. I want to compare the distribution of usage for different sets of individuals to see if there is something inherently different about the 15 new individuals.

New vs old peaks

I want to compare the usage of the new peaks compared to the overall mean usage. To do this I need to seperate the new and old PAS.

newPAS5perc=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names = c("chr", "start","end", "ID", "score", "strand"))
oldPAS5perc=read.table("../../threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", stringsAsFactors = F, col.names = c("chr", "start", "end", "ID", "score", "strand"))

uniqnew=newPAS5perc %>% semi_join(oldPAS5perc, by=c("chr", "start", "end"))

Pull in the usage of the peaks:

Total

totalPeakUs=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "loc", "strand", "peak"))
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
ind=colnames(totalPeakUs)[8:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric", col.names = ind)


#numeric with anno
totalPeak=as.data.frame(cbind(totalPeakUs[,1:7], totalPeakUs_CountNum))

totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)

#append mean to anno
TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:7],mean=totalPeakUs_CountNum_mean))
uniqnewPasnum=uniqnew  %>% separate(ID ,into=c("peaknum", "geneloc"),sep=":") %>% mutate(peak=paste("peak", peaknum, sep="")) %>% select(peak)

Filter these inthe mean usage:

TotalPeakUSMeanClass= TotalPeakUSMean %>% mutate(New=ifelse(peak %in% uniqnewPasnum$peak,"new", "original")) %>% mutate(Cutoff=ifelse(mean>=.05, "Yes", "No"))

mean(TotalPeakUSMean$mean)
[1] 0.2378282

Plot:

ggplot(TotalPeakUSMeanClass, aes(y=mean,x=New)) + geom_violin() + geom_hline(yintercept = mean(TotalPeakUSMean$mean), col="red") + facet_grid(~Cutoff)

This shows me the new peaks are not the peaks that barely passed the cuttoff before. These peaks cover the distribution of usage.


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:
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 [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:
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loaded via a namespace (and not attached):
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