Quantify reads per Peaks

Last updated: 2018-08-24

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File	Version	Author	Date	Message
Rmd	43f6773	brimittleman	2018-08-24	prenormalization PCA
html	5b4893e	brimittleman	2018-08-24	Build site.
Rmd	679aef4	brimittleman	2018-08-24	initalize reads per peak analysis and update index

In this analysis I will run feature counts on the human and chimp,total and nuclear threeprime seq libraries agaisnt the orthologous peaks I called with liftover.

First I will need to convert the bed files to saf files. This File is GeneID, Chr, Start, End, Strand. In my case it is peak ID.


#human
from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.bed"):
    chrom, start, end, name = ln.split()
    start=int(start)
    end=int(end)
    fout.write("%s\t%s\t%d\t%d\t.\n"%(name, chrom, start, end))

fout.close()


#chimp
from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.bed"):
    chrom, start, end, name = ln.split()
    start=int(start)
    end=int(end)
    fout.write("%s\t%s\t%d\t%d\t.\n"%(name, chrom, start, end))

fout.close()

The resulting files are:

/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.saf
/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.saf

#!/bin/bash

#SBATCH --job-name=fc_orthopeaks
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=fc_orthopeaks.out
#SBATCH --error=fc_orthopeaks.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END


module load Anaconda3
source activate comp_threeprime_env

# outdir: /project2/gilad/briana/comparitive_threeprime/data/Peak_quant

#-s 0 is unstranded 

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/HumanTotal_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/human/data/sort/*T-sort.bam -s 0

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/HumanNuclear_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/human/data/sort/*N-sort.bam -s 0


featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/ChimpTotal_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*T-sort.bam -s 0

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/ChimpNuclear_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*N-sort.bam -s 0

I need the matching peaks from human and chimps from the liftover pipeline data.

library(workflowr)

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

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(reshape2)


Attaching package: 'reshape2'

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

    smiths

library(cowplot)


Attaching package: 'cowplot'

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

    ggsave

PeakNames=read.table(file = "../data/liftover/HumanChimpPeaknames.txt", header=T, stringsAsFactors = F)

Chimps:

18358
3622
3659
4973
pt30
pt91

Humans:

18498
18499
18502
18504
18510
18523

namesHN= c("human","Chr", "Start", "End", "Strand", "Length", "N18498", "N18499", "N18502", "N18504", "N18510", "N18523")
humanNuc=read.table("../data/orthoPeak_quant/HumanNuclear_Orthopeak.quant", header = T, stringsAsFactors = F, col.names = namesHN)

namesHT= c("human","Chr", "Start", "End", "Strand", "Length", "T18498", "T18499", "T18502", "T18504", "T18510", "T18523")
humanTot=read.table("../data/orthoPeak_quant/HumanTotal_Orthopeak.quant", header=T, stringsAsFactors = F, col.names = namesHT)


namesCN= c("chimp","Chr", "Start", "End", "Strand", "Length", "N18358", "N3622", "N3659", "N4973", "Npt30", "Npt91")

chimpNuc=read.table("../data/orthoPeak_quant/ChimpNuclear_Orthopeak.quant", header = T,stringsAsFactors = F, col.names = namesCN)

namesCT= c("chimp","Chr", "Start", "End", "Strand", "Length", "T18358", "T3622", "T3659", "T4973", "Tpt30", "Tpt91")
chimpTot=read.table("../data/orthoPeak_quant/ChimpTotal_Orthopeak.quant", header=T, stringsAsFactors = F, col.names = namesCT)

I need to add the human names to the chimp file and chimp names to the human files.

humanNuc_ed= humanNuc %>% inner_join(PeakNames, by="human") %>% mutate(ID=paste(human,chimp, sep=":")) %>% select("ID",  "N18498", "N18499", "N18502", "N18504", "N18510", "N18523")

humanTot_ed= humanTot %>% inner_join(PeakNames, by="human") %>%mutate(ID=paste(human,chimp, sep=":")) %>% select("ID",  "T18498", "T18499", "T18502", "T18504", "T18510", "T18523")

chimpNuc_ed=chimpNuc %>% inner_join(PeakNames, by="chimp") %>% mutate(ID=paste(human,chimp, sep=":")) %>% select("ID", "N18358", "N3622", "N3659", "N4973", "Npt30", "Npt91")

chimpTot_ed= chimpTot %>% inner_join(PeakNames, by="chimp") %>% mutate(ID=paste(human,chimp, sep=":")) %>% select("ID", "T18358", "T3622", "T3659", "T4973", "Tpt30", "Tpt91")

Now I need to join all of these together by the peaks.

allPeakQuant= humanNuc_ed %>% left_join(humanTot_ed, by="ID") %>% left_join(chimpNuc_ed, by="ID") %>% left_join(chimpTot_ed, by="ID") %>% column_to_rownames(var="ID")
allPeakQuant_matrix=as.matrix(allPeakQuant)

Run PCA:

humans=c("N18498", "N18499", "N18502", "N18504", "N18510", "N18523", "T18498", "T18499", "T18502", "T18504", "T18510", "T18523")
pc=prcomp(allPeakQuant_matrix)
rotation=data.frame(pc$rotation) %>% rownames_to_column(var="Sample") %>% mutate(Fraction=ifelse(grepl("N", Sample), "Nuclear", "Total")) %>% mutate(Species= ifelse(Sample %in% humans, "Human", "Chimp"))

Plot this:

prenormPCA=ggplot(rotation, aes(x=PC1, y=PC2, col=Species, shape=Fraction)) + geom_point() + labs(title="Prenormalization PCA")

prenormPCA34=ggplot(rotation, aes(x=PC3, y=PC4, col=Species, shape=Fraction)) + geom_point() + labs(title="Prenormalization PCA")

prenormPCA56=ggplot(rotation, aes(x=PC5, y=PC6, col=Species, shape=Fraction)) + geom_point() + labs(title="Prenormalization PCA")

prenormPCA78=ggplot(rotation, aes(x=PC7, y=PC8, col=Species, shape=Fraction)) + geom_point() + labs(title="Prenormalization PCA")

plot_grid(prenormPCA,prenormPCA34, prenormPCA56, prenormPCA78)

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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_1.3.1   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_1.1.1

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_2.1.19      
 [7] rlang_0.2.1       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.6.0    
[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     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.18      scales_0.5.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.15     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.0         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    crayon_1.3.4     
[40] whisker_0.3-2     pkgconfig_2.0.1   xml2_1.2.0       
[43] lubridate_1.7.4   assertthat_0.2.0  rmarkdown_1.10   
[46] httr_1.3.1        rstudioapi_0.7    R6_2.2.2         
[49] nlme_3.1-137      git2r_0.23.0      compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1

Quantify reads per Peaks

Briana Mittleman

8/24/2018

Session information