Peak QC data and figures

Last updated: 2019-01-16

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    Modified:   analysis/understandPeaks.Rmd
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File	Version	Author	Date	Message
Rmd	6aa94e4	Briana Mittleman	2019-01-16	plots for 5% usage

I want to do some QC and filtering on the peaks to go along with the number of peaks to cover % of a gene figure.

Number of called peaks
peaks used at X% in total/nuclear
number of genes

library(tidyverse)

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

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0

── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(data.table)


Attaching package: 'data.table'

The following objects are masked from 'package:dplyr':

    between, first, last

The following object is masked from 'package:purrr':

    transpose

library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

totalPeakUs=read.table("../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))
nuclearPeakUs=read.table("../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))

There are 338141 called peaks in the data.

I need to make the fractions numeric, I will do this in python because I can go through each value, split them and get the numeric.

It will be easiest if I write the counts out:

#write.table(totalPeakUs[,7:45], file="../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.CountsOnly",quote=FALSE, col.names = F, row.names = F)

#write.table(nuclearPeakUs[,7:45], file="../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.CountsOnly",quote=FALSE, col.names = F, row.names = F)

Move these to /project2/gilad/briana/threeprimeseq/data/PeakUsage

convertCount2Numeric.py

def convert(infile, outfile):
  final=open(outfile, "w")
  for ln in open(infile, "r"):
    line_list=ln.split()
    new_list=[]
    for i in line_list:
      num, dem = i.split("/")
      if dem == "0":
        perc = "0.00"
      else:
        perc = int(num)/int(dem)
        perc=round(perc,2)
        perc= str(perc)
      new_list.append(perc)
    final.write("\t".join(new_list)+ '\n')
  final.close()
  
convert("/project2/gilad/briana/threeprimeseq/data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.CountsOnly","/project2/gilad/briana/threeprimeseq/data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.CountsOnlyNUMERIC.txt" )


convert("/project2/gilad/briana/threeprimeseq/data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.CountsOnly","/project2/gilad/briana/threeprimeseq/data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.CountsOnlyNUMERIC.txt")

Because any value less than .001 becomes 0, all peaks for a gene will not add to zero.

ind=colnames(totalPeakUs)[7:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.CountsOnlyNUMERIC.txt", col.names = ind)

nuclearPeakUs_CountNum=read.table("../data/PeakUsage/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.CountsOnlyNUMERIC.txt", col.names = ind)

Numeric values with the annotations:

totalPeak=as.data.frame(cbind(totalPeakUs[,1:6], totalPeakUs_CountNum))
nuclearPeak=as.data.frame(cbind(nuclearPeakUs[,1:6], nuclearPeakUs_CountNum))

Get the mean coverage for each peak.

totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)
nuclearPeakUs_CountNum_mean=rowMeans(nuclearPeakUs_CountNum)

Append these to the inforamtion about the peak.

TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:6],totalPeakUs_CountNum_mean))
NuclearPeakUSMean=as.data.frame(cbind(nuclearPeakUs[,1:6],nuclearPeakUs_CountNum_mean))

Get the number of genes with mean(usage > 5%)

Total:

TotalPeakUSMean_filt=TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())

I want to get how many genes have 1,2,3,4 ect:

totalPeaksPerGene=TotalPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())

ggplot(totalPeaksPerGene,aes(x=Npeaks,y=GenesWithNPeaks)) + geom_bar(stat="identity",fill="darkviolet") + labs(x="Number Peaks with >5% usage", y="Number of Genes", title="Genes with peaks covering > 5% in Total")

Nuclear:

NuclearPeakUSMean_filt=NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())

I want to get how many genes have 1,2,3,4 ect:

nuclearPeaksPerGene=NuclearPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaksPerGene$GenesWithNPeaks=as.integer(nuclearPeaksPerGene$GenesWithNPeaks)
ggplot(nuclearPeaksPerGene,aes(x=Npeaks,y=GenesWithNPeaks)) + geom_bar(stat="identity", fill="deepskyblue3") + labs(x="Number Peaks with >5% usage", y="Number of Genes", title="Genes with peaks covering > 5% in Nuclear")

Genes with at least 1:

#nuclear
nrow(NuclearPeakUSMean_filt)

[1] 15431

#total
nrow(TotalPeakUSMean_filt)

[1] 15435

Join them to put on the same plot:
gene level

nPeaksBoth_gene=TotalPeakUSMean_filt %>% full_join(NuclearPeakUSMean_filt, by="gene")
colnames(nPeaksBoth_gene)= c("Gene", "Total", "Nuclear")
nPeaksBoth_gene$Nuclear= nPeaksBoth_gene$Nuclear %>% replace_na(0)
nPeaksBoth_gene$Total= nPeaksBoth_gene$Total %>% replace_na(0)

peak number level:

nPeaksBoth=totalPeaksPerGene %>% full_join(nuclearPeaksPerGene, by="Npeaks")
colnames(nPeaksBoth)= c("Peaks", "Total", "Nuclear")
nPeaksBoth$Total= nPeaksBoth$Total %>% replace_na(0)

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

Make a plot:

peakUsage5perc=ggplot(nPeaksBoth_melt, aes(x=Peaks, y=Genes, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + labs(title="Number of Genes with >5% Peak Usage") + 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/QC_plots/peakUsage5perc.png")

Saving 7 x 5 in image

Peaks with >5 per not at gene level:

#nuclear  
NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% nrow()

[1] 58494

#total
TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% nrow()

[1] 49234

Session information

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    workflowr_1.1.1   data.table_1.11.8
 [4] forcats_0.3.0     stringr_1.3.1     dplyr_0.7.6      
 [7] purrr_0.2.5       readr_1.1.1       tidyr_0.8.1      
[10] tibble_1.4.2      ggplot2_3.0.0     tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  reshape2_1.4.3    haven_1.1.2      
 [4] lattice_0.20-35   colorspace_1.3-2  htmltools_0.3.6  
 [7] yaml_2.2.0        rlang_0.2.2       R.oo_1.22.0      
[10] pillar_1.3.0      glue_1.3.0        withr_2.1.2      
[13] R.utils_2.7.0     modelr_0.1.2      readxl_1.1.0     
[16] bindr_0.1.1       plyr_1.8.4        munsell_0.5.0    
[19] gtable_0.2.0      cellranger_1.1.0  rvest_0.3.2      
[22] R.methodsS3_1.7.1 evaluate_0.11     labeling_0.3     
[25] knitr_1.20        broom_0.5.0       Rcpp_0.12.19     
[28] scales_1.0.0      backports_1.1.2   jsonlite_1.5     
[31] hms_0.4.2         digest_0.6.17     stringi_1.2.4    
[34] grid_3.5.1        rprojroot_1.3-2   cli_1.0.1        
[37] tools_3.5.1       magrittr_1.5      lazyeval_0.2.1   
[40] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[46] rmarkdown_1.10    httr_1.3.1        rstudioapi_0.8   
[49] R6_2.3.0          nlme_3.1-137      git2r_0.23.0     
[52] compiler_3.5.1

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Peak QC data and figures

Briana Mittleman

1/16/2019

Session information