Last updated: 2019-03-11

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Knit directory: threeprimeseq/analysis/

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File Version Author Date Message
Rmd 8871122 Briana Mittleman 2019-03-11 subset to x% 
library(workflowr)
This is workflowr version 1.2.0
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library(tidyverse)
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I want to collect the peaks up to X% usage and show the total/nuclear 3’ seq and RNA seq read buildup at these peaks.

To do this I need all of the peaks, i can group by mean usage and look at cumulative sum. I will have to think about the + and - peaks.

I will do this based on the nuclear mean usage.

NucMeanPeakUsage=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "gene", "strand", "peak", "meanUsage"))

NucMeanPeakUsage_neg=NucMeanPeakUsage %>% filter(strand=="-")
NucMeanPeakUsage_pos=NucMeanPeakUsage %>% filter(strand=="+")

Do the positive strand first:

I should only do this for genes with at least 2 peaks

NucMeanPeakUsage_pos_10perc=NucMeanPeakUsage_pos %>% group_by(gene) %>% mutate(CumMean=cumsum(meanUsage), nPeaks=n()) %>%
 filter(nPeaks>1) %>% filter(CumMean < .1)


NucMeanPeakUsage_pos_50perc=NucMeanPeakUsage_pos %>% group_by(gene) %>% mutate(CumMean=cumsum(meanUsage), nPeaks=n()) %>%
 filter(nPeaks>1) %>% filter(CumMean < .5)

For the negative strand:

NucMeanPeakUsage_neg_10perc=NucMeanPeakUsage_neg %>% group_by(gene) %>% mutate(CumMean=rev(cumsum(rev(meanUsage))), nPeaks=n()) %>% filter(nPeaks>1)  %>% filter(CumMean < .1)

NucMeanPeakUsage_neg_50perc=NucMeanPeakUsage_neg %>% group_by(gene) %>% mutate(CumMean=rev(cumsum(rev(meanUsage))), nPeaks=n()) %>% filter(nPeaks>1)  %>% filter(CumMean < .5)

I will plot the up to 50% peaks first:

Join pos and neg

NucMeanPeakUsage_50perc =as.data.frame(rbind(NucMeanPeakUsage_neg_50perc, NucMeanPeakUsage_pos_50perc))


NucMeanPeakUsage_10perc =as.data.frame(rbind(NucMeanPeakUsage_neg_10perc, NucMeanPeakUsage_pos_10perc))




#write.table(NucMeanPeakUsage_50perc, file="../data/PeakUsage_noMP_GeneLocAnno/NucPeaksTo50perc.txt", sep="\t", row.names = F, col.names = F,quote=F)
#write.table(NucMeanPeakUsage_10perc, file="../data/PeakUsage_noMP_GeneLocAnno/NucPeaksTo10perc.txt", sep="\t", row.names = F, col.names = F,quote=F)

I want to filter the deep tools peaks on these:

/project2/gilad/briana/threeprimeseq/data/peaks4DT/NucPeaksTo50perc.txt

/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_allPeaks_fixedStrand.bed

subsetPeak_uptoXper.py

def main(filterFile, outfile):
  inBed=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_allPeaks_fixedStrand.bed", "r")
  outF=open(outfile, "w")
  filt=open(filterFile,"r")
  okDic={}
  for ln in filt: 
    peak=ln.split()[5]
    okDic[peak]=""
  for ln in inBed: 
    peak=ln.split()[3].split(":")[0]
    if peak in okDic.keys():
        outF.write(ln)
  outF.close()    


if __name__ == "__main__":
    import sys
    import pybedtools
    filterFile=sys.argv[1]
    outfile=sys.argv[2]
    main(filterFile, outfile)

Run this with the 50% and 10% peaks

python subsetPeak_uptoXper.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/NucPeaksTo50perc.txt /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto50perc_fixedStrand.bed
python subsetPeak_uptoXper.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/NucPeaksTo10perc.txt /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto10perc_fixedStrand.bed

Run the deeptools plots

DTPlot_Nuc50percCov.sh

#!/bin/bash

#SBATCH --job-name=DTPlot_Nuc50percCov
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=DTPlot_Nuc50percCov.out
#SBATCH --error=DTPlot_Nuc50percCov.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto50perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc50perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc50perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "Combined Reads at PAS up to 50%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc50perc_Nompfilt.png

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto50perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc50perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc50perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "RNA seq at PAS up to 50%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc50perc_Nompfilt.png

DTPlot_Nuc10percCov.sh

#!/bin/bash

#SBATCH --job-name=DTPlot_Nuc10percCov
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=DTPlot_Nuc10percCov.out
#SBATCH --error=DTPlot_Nuc10percCov.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto10perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc10perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc10perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "Combined Reads at PAS up to 10%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nuc10perc_Nompfilt.png

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakto10perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc10perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc10perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "RNA seq at PAS up to 10%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nuc10perc_Nompfilt.png

Top ten (>90)

NucMeanPeakUsage_neg_top10perc=NucMeanPeakUsage_neg %>% group_by(gene) %>% mutate(CumMean=rev(cumsum(rev(meanUsage))), nPeaks=n()) %>% filter(nPeaks>1)  %>% filter(CumMean > .9)

NucMeanPeakUsage_pos_top10perc=NucMeanPeakUsage_pos %>% group_by(gene) %>% mutate(CumMean=cumsum(meanUsage), nPeaks=n()) %>%
 filter(nPeaks>1) %>% filter(CumMean <.9)


NucMeanPeakUsage_top10perc =as.data.frame(rbind(NucMeanPeakUsage_neg_top10perc, NucMeanPeakUsage_pos_top10perc))

write.table(NucMeanPeakUsage_top10perc, file="../data/PeakUsage_noMP_GeneLocAnno/NucPeaksTOP10perc.txt", sep="\t", row.names = F, col.names = F,quote=F)
python subsetPeak_uptoXper.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/NucPeaksTOP10perc.txt /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakTOP10perc_fixedStrand.bed

DTPlot_NucTop10percCov.sh

#!/bin/bash

#SBATCH --job-name=DTPlot_NucTop10percCov
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=DTPlot_NucTop10percCov.out
#SBATCH --error=DTPlot_NucTop10percCov.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakTOP10perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nucTop10perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nucTop10perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "Combined Reads at PAS up to 10%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/BothFrac_nucTop10perc_Nompfilt.png

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_NucPeakTOP10perc_fixedStrand.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nucTop10perc_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nucTop10perc_Nompfilt.gz --refPointLabel "PAS" --plotTitle "RNA seq at PAS up to 10%" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/DT_up2Xperc/RNA_nucTop10perc_Nompfilt.png




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] cowplot_0.9.4   reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.0.1   purrr_0.3.1     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.0.1    ggplot2_3.1.0   tidyverse_1.2.1 workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.13    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0       haven_2.1.0     
[21] xfun_0.5         withr_2.1.2      xml2_1.2.0       httr_1.4.0      
[25] knitr_1.21       hms_0.4.2        generics_0.0.2   fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.4.0         readxl_1.3.0     rmarkdown_1.11   modelr_0.1.4    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.3  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.4-0
[45] stringi_1.3.1    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4