Last updated: 2019-02-16

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Knit directory: threeprimeseq/analysis/

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Rmd f8c76ea Briana Mittleman 2019-02-16 move peak QC plots

library(workflowr)
This is workflowr version 1.2.0
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library(tidyverse)
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library(reshape2)

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library(cowplot)

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I want to remake a lot of the peak QC plots I have been making with the new mapped and proccessed data created in the accounting for mappping bias analysis

  • Peaks per gene

  • Number of genes with 1 peak, 2 peaks, more peaks

  • Distance between gene and TES

  • Peaks in each category

  • Peak Size

Peak per gene:

I will do this for total and nuclear 5% seperatly then for the peaks I used in the QTL analysis.

Nuclear peaks: 42127: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt

Total peaks: 36915: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt

peakNames=c("chr", 'start','end','gene','strand','name', 'mean')
totalPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)
nuclearPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)

Peaks per gene:

totalPeaks_genes=totalPeaks %>% group_by(gene) %>% summarise(nPeaks=n()) %>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaks_genes=nuclearPeaks %>% group_by(gene) %>% summarise(nPeaks=n())%>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())

nPeaksBoth=totalPeaks_genes %>% full_join(nuclearPeaks_genes, by="nPeaks")

colnames(nPeaksBoth)= c("Npeaks", "Total", "Nuclear")
nPeaksBoth$Total= nPeaksBoth$Total %>% replace_na(0)

#melt nPeaksBoth
nPeaksBoth_melt=melt(nPeaksBoth, id.var="Npeaks")
colnames(nPeaksBoth_melt)= c("PAS", "Fraction", "Genes")

peakUsage5perc=ggplot(nPeaksBoth_melt, aes(x=PAS, y=Genes, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + labs(title="Number of Genes by PAS Number \n 5% Usage",x="Number of PAS in Gene") + theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold"))+ scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + facet_grid(~Fraction)

peakUsage5perc

ggsave(peakUsage5perc, file="../output/plots/PeakNumberPerGenebyFrac.png")
Saving 7 x 5 in image

Plot this with the peaks used in the fraction

allPeaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", stringsAsFactors = F, col.names = c("chr", 'start','end', 'id', 'score', 'strand')) %>% separate(id, into=c("gene", "peak"), sep=":")%>% group_by(gene) %>% summarise(nPeaks=n()) %>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())

colnames(allPeaks)=c("PAS","Genes" )

allPeaksGenes=ggplot(allPeaks, aes(x=PAS, y=Genes)) + geom_bar(stat="identity",fill="blue") + labs(title="Number of Genes by PAS Count: \n PAS Used in QTL analysis",x="Number of PAS in Gene") + theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold")) 

allPeaksGenes

ggsave(allPeaksGenes, file="../output/plots/PeakNumberPerGeneUsedinQTL.png")
Saving 7 x 5 in image

Number of genes with 1 peak, 2 peaks, more peaks

Make this as a boxplot

GeneAnno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", stringsAsFactors = F, header=T) %>% select(name2) %>%  unique()
colnames(GeneAnno)="gene"
genesWithpeak= read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", stringsAsFactors = F, col.names = c("chr", 'start','end', 'id', 'score', 'strand')) %>% separate(id, into=c("gene", "peak"), sep=":") %>% select(gene) %>% unique()

Geneswith0= GeneAnno %>% anti_join(genesWithpeak, by="gene") %>% nrow()
Geneswith0
[1] 11896

To get the genes with 0 peaks I need to pull in the gene annotation file

morethan2= allPeaks %>% filter(PAS > 2) 
colSums(morethan2)
  PAS Genes 
  102  7361 
Category=c("0 PAS", "1 PAS", "2 PAS", "More than 2 PAS")
genesPerCat=c(11896/27115, 4909/27115, 2949/27115, 7361/27115)

genesPerCat_df=as.data.frame(cbind(Category,genesPerCat))

genesPerCat_df$genesPerCat=as.numeric(as.character(genesPerCat_df$genesPerCat))

lab0=paste("Genes =", "11896", sep=" ")
lab1=paste("Genes =", "4909", sep=" ")
lab2=paste("Genes =", "2949", sep=" ")
labMore=paste("Genes =", "7361", sep=" ")


propGenesbyPAS=ggplot(genesPerCat_df, aes(x="", y=genesPerCat, fill=Category)) + geom_bar(stat="identity") + labs(x="Total Genes = 27115", y="Proportion of Genes", title="Proportion of Genes by number of PAS") + annotate("text", x="", y= .7, label=lab0) + annotate("text", x="", y= .5, label=lab1) + annotate("text", x="", y= .33, label=lab2) + annotate("text", x="", y= .2, label=labMore)

propGenesbyPAS

ggsave(propGenesbyPAS, file="../output/plots/PropOfGenesByPASnum.png")
Saving 7 x 5 in image

Distance between TES and peak

  • GetDistTXNend2Peak.py

convert /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF to bed file

peaksGeneLocAnno_5percSAF2Bed.py

distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)
txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")
distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))
mean(txnanno$length)
[1] 60808.79
distTXN2PeakPlot=ggplot(distTXN2Peak, aes(x=AbsDist + 1)) + geom_density() + scale_x_log10() + labs(x="Absolute Distance between end of Transcription and center of Peak", title="Distribution of transcription to peak absolute distance") +  geom_vline(xintercept=mean(txnanno$length), col="red") + annotate("text", x=1000000, y=.4, label="Average transcript length \n for genes in peaks", col='red')

distTXN2PeakPlot

ggsave(distTXN2PeakPlot, file="../output/plots/DistanceBetweenPeakandTES.png")
Saving 7 x 5 in image

Peaks per category

  • processGenLocPeakAnno2SAF_withAnno.py
  • filternamePeaks5percCov_GeneLocAnno_withAnno.py
peakswAnno=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov_withAnno.SAF", header=T) %>% separate(GeneID, into=c("Peak", "chrom", "start", "end", "strand", "gene", "loc"),sep=":") %>% select(Peak, loc) %>% group_by(loc) %>% summarise(Num=n())
locationOfPeaks=ggplot(peakswAnno, aes(x=loc, y=Num)) + geom_bar(stat="identity", fill="blue") + labs(x="Gene Location", y="Number of Peaks", title="Location distribution for all PAS with 5% Usage")
locationOfPeaks

ggsave(locationOfPeaks, file="../output/plots/PeakLocationByAnnotation.png")
Saving 7 x 5 in image

Peak Size

Peak length:

peaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed",col.names=c("chr", 'start','end', 'peak', 'score', 'strand')) %>% mutate(length=end-start)
ggplot(peaks,aes(x=length)) + geom_histogram(bins=300) + labs(title="Peak Size", x="number of basepairs") + geom_vline(xintercept =mean(peaks$length),col="red")


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  cowplot_0.9.3   reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4 haven_1.1.2      lattice_0.20-35  colorspace_1.3-2
 [5] htmltools_0.3.6  yaml_2.2.0       rlang_0.2.2      pillar_1.3.0    
 [9] glue_1.3.0       withr_2.1.2      modelr_0.1.2     readxl_1.1.0    
[13] bindr_0.1.1      plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[17] cellranger_1.1.0 rvest_0.3.2      evaluate_0.13    labeling_0.3    
[21] knitr_1.20       broom_0.5.0      Rcpp_0.12.19     scales_1.0.0    
[25] backports_1.1.2  jsonlite_1.6     fs_1.2.6         hms_0.4.2       
[29] digest_0.6.17    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[33] cli_1.0.1        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[37] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[41] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.11   httr_1.3.1      
[45] rstudioapi_0.9.0 R6_2.3.0         nlme_3.1-137     git2r_0.24.0    
[49] compiler_3.5.1