Quantify reads per Peaks

Last updated: 2018-08-24

workflowr checks: (Click a bullet for more information)

✔ R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed: set.seed(20180801)

The command set.seed(20180801) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: 32a9409
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
```
Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  com_threeprime.Rproj
    Untracked:  data/PeakPerExon/
    Untracked:  data/PeakPerGene/
    Untracked:  data/dist_TES/
    Untracked:  data/dist_upexon/
    Untracked:  data/liftover/
    Untracked:  data/map.stats.csv
    Untracked:  data/map.stats.xlsx
    Untracked:  data/orthoPeak_quant/

Unstaged changes:
    Deleted:    comparitive_threeprime.Rproj
```
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	32a9409	brimittleman	2018-08-24	compare raw to cpm
html	bc94507	brimittleman	2018-08-24	Build site.
Rmd	189a2fb	brimittleman	2018-08-24	organize
html	d0e92f5	brimittleman	2018-08-24	Build site.
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

Feature count

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

Visualize raw counts

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)

Expand here to see past versions of unnamed-chunk-10-1.png:

Version	Author	Date
d0e92f5	brimittleman	2018-08-24

Normalize counts

To normalize this i will need to understand the distributions. I am not sure if this data will be normal.

library(edgeR)

Loading required package: limma

allPeakQuant_matrix_cpm=cpm(allPeakQuant_matrix, log=T)

plotDensities(allPeakQuant_matrix_cpm, legend = "bottomright", main="Pre-filtering")
abline(v = 0, lty = 3)

par(mfrow=c(2,1))
boxplot(log10(allPeakQuant + 1), main="Log Raw Counts + 1")
boxplot(allPeakQuant_matrix_cpm, main="Log CPM Counts")

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] edgeR_3.22.3    limma_3.36.2    bindrcpp_0.2.2  cowplot_0.9.3  
 [5] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6    
 [9] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
[13] ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.1.1

loaded via a namespace (and not attached):
 [1] locfit_1.5-9.1    tidyselect_0.2.4  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_2.6.0     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_0.12.18     
[28] scales_1.0.0      backports_1.1.2   jsonlite_1.5     
[31] hms_0.4.2         digest_0.6.16     stringi_1.2.4    
[34] grid_3.5.1        rprojroot_1.3-2   cli_1.0.0        
[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_0.7   
[49] R6_2.2.2          nlme_3.1-137      git2r_0.23.0     
[52] 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

Feature count

Visualize raw counts

Normalize counts

Session information