Last updated: 2019-02-16
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Knit directory: threeprimeseq/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | f8c76ea | Briana Mittleman | 2019-02-16 | move peak QC plots |
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(tidyverse)
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library(reshape2)
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library(cowplot)
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ggsave
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
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
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
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
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")
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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