Distal vs Proximal Peak DeepTools

Last updated: 2019-01-20

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I want to show RNA seq vs 3’ seq in peaks that are internal.

To do this I need to pull in the peaks (use the filtered ones from peakQCPlots) to get peaks that are used but are not the most distal for the gene. I need to split by strand when i do this becasue most distal is different in both cases.

This analysis is updated post cleaning for mispriming

Load Libraries

library(data.table)
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::between()   masks data.table::between()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::first()     masks data.table::first()
✖ dplyr::lag()       masks stats::lag()
✖ dplyr::last()      masks data.table::last()
✖ purrr::transpose() masks data.table::transpose()

library(workflowr)

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

library(cowplot)

Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Load peaks for total and nuclear post filter:

name=c("chr","start","end", "gene", "strand", "peak", "meanUsage")
total_PeakUsMean=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", head = F,col.names = name)
nuclear_PeakUsMean=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", head=F,col.names = name)

Seperate positive and negative:

• Total

total_PeakUsMean_pos=total_PeakUsMean %>% filter(strand=="+")
total_PeakUsMean_neg=total_PeakUsMean %>% filter(strand=="-")

• Nuclear

nuclear_PeakUsMean_pos=nuclear_PeakUsMean %>% filter(strand=="+")
nuclear_PeakUsMean_neg=nuclear_PeakUsMean %>% filter(strand=="-")

Group by gene and keep internal (remove genes with only 1)

• For positive strand keep bottom one with top_n() peak # (most internal)

total_PeakUsMean_pos_internal=total_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

nuclear_PeakUsMean_pos_internal=nuclear_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

*For negative strand use top_n(-1)

total_PeakUsMean_neg_internal=total_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

nuclear_PeakUsMean_neg_internal=nuclear_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

Bind the total and nuclear rows back together:

Total

total_PeakUsMean_internal=as.data.frame(rbind(total_PeakUsMean_pos_internal,total_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)

write.table(total_PeakUsMean_internal, file="../data/PeakUsage_noMP/InternalFilteredPeak.total.txt", row.names = F, col.names = F, quote = F)

Nuclear:

nuclear_PeakUsMean_internal=as.data.frame(rbind(nuclear_PeakUsMean_pos_internal,nuclear_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)


write.table(nuclear_PeakUsMean_internal, file="../data/PeakUsage_noMP/InternalFilteredPeak.nuclear.txt", row.names = F, col.names = F, quote = F)

Use these peak numbers to filter the bed file that I use for deep tools. I can do this in python with a dictionary of the peaks to keep.

The file I want to subset is /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

filterPeaksInner_noMP.py

#python  

peaksIn="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.bed"
innerPeaksTotal="/project2/gilad/briana/threeprimeseq/data/PeakUsage/InternalFilteredPeak.total.txt"
innerPeaksNuclear="/project2/gilad/briana/threeprimeseq/data/PeakUsage/InternalFilteredPeak.nuclear.txt"

OkPeakTot={}
for ln in open(innerPeaksTotal, "r"):
    peaknum=ln.split()[5]
    peaknumOnly=peaknum[4:]
    OkPeakTot[peaknumOnly] = ""
  
OkPeakNuc={}
for ln in open(innerPeaksNuclear, "r"):
   peaknum=ln.split()[5]
   peaknumOnly=peaknum[4:]
   OkPeakNuc[peaknumOnly]=""
   
TotOut=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed", "w")

NucOut=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed", "w")


#read peaks and write out to each file 

for ln in open(peaksIn,"r"):
    peakName=ln.split()[3]
    peakNameStr=str(peakName)
    if peakName in OkPeakTot.keys():
        TotOut.write(ln)
    if peakName in OkPeakNuc.keys():
        NucOut.write(ln)

TotOut.close()
NucOut.close()
      

Now I can make the deep tools plots:

Three prime seq with inner total

TotalDTPlotmyPeaks_Internal.sh

#!/bin/bash

#SBATCH --job-name=TotalDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TotalDTPlotmyPeaks_Internal.out
#SBATCH --error=TotalDTPlotmyPeaks_Internal.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  -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed -b 1000 -a 1000  --outFileName /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_Internal.gz  

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_Internal.gz --refPointLabel "Internal Peaks" --plotTitle "Total Combined Reads Internal Peaks" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_internal.png

NuclearDTPlotmyPeaks_Internal.sh

#!/bin/bash

#SBATCH --job-name=NuclearDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucelarDTPlotmyPeaks_Internal.out
#SBATCH --error=NucelarDTPlotmyPeaks_Internal.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/Nucelar_MergedBamCoverage.bw  -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed -b 1000 -a 1000  --outFileName /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_Internal.gz  

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_Internal.gz --refPointLabel "Internal Peaks" --plotTitle "Nuclear Combined Reads Internal Peaks" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_internal.png

RNA seq plot with the total internal peaks

RNAseqDTPlotmyPeaks_Internal.sh

#!/bin/bash

#SBATCH --job-name=RNAseqDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNAseqDTPlotmyPeaks_Internal.out
#SBATCH --error=RNAseqDTPlotmyPeaks_Internal.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw   -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.gz --refPointLabel "Total Internal Peaks" --plotTitle "Combined RNAseq Reads at Total Internal"  --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.png

RNAseqDTPlotmyPeaks_NuclearInternal.sh

#!/bin/bash

#SBATCH --job-name=RNAseqDTPlotmyPeaks_NuclearInternal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNAseqDTPlotmyPeaks_NuclearInternal.out
#SBATCH --error=RNAseqDTPlotmyPeaks_NuclearInternal.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw   -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.gz --refPointLabel "Nucelar Internal Peaks" --plotTitle "Combined RNAseq Reads at Nuclear Internal"  --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.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] bindrcpp_0.2.2    cowplot_0.9.3     workflowr_1.1.1  
 [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  
[13] data.table_1.11.8

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

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