Last updated: 2018-06-22

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The goal of this analysis is to explore the second batch of pilot netseq data (net4) with the 3 lanes of the original line. This data has been run on 3 lanes.

Net4 lines * 19238
* 19223
* 18497
* 19209
* 18500
* 18870
* 19225
* 18853

Feature counts on protein coding genes

I want to use feature counts to summarize how many counds we have in each protien coding gene. There are 20,347 genes in the annotation file.

Make an SAF file instead: Gene id, Chr, Start, End, Strand from the gencode.v19.annotation.proteincodinggene.bed


awk 'BEGIN {print "GeneID" "\t" "Chr" "\t" "Start" "\t" "End" "\t" "Strand"} {print $4 "\t" $1 "\t" $2 "\t" $3 "\t" $6}' gencode.v19.annotation.proteincodinggene.bed  >gencode.v19.annotation.proteincodinggene.saf

fc_gene.sh

#!/bin/bash

#SBATCH --job-name=FC_genes
#SBATCH --time=8:00:00
#SBATCH --partition=gilad
#SBATCH --output=fc_gene.out
#SBATCH --error=fc_gene.err
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate net-seq 

#input is a bam file 
sample=$1


describer=$(echo ${sample} | sed -e 's/.*\YG-SP-//' | sed -e "s/_combined_Netpilot-sort.bam$//")


featureCounts -T 5 -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.saf -F 'SAF' -g 'GeneID' -o /project2/gilad/briana/Net-seq-pilot/data/fc_genecov/genecov.${describer}.txt $1

test on: /project2/gilad/briana/Net-seq-pilot/data/sort/YG-SP-NET3-19257_combined_Netpilot-sort.bam

Create a wrapper:

#!/bin/bash

#SBATCH --job-name=w_fcgenes
#SBATCH --time=8:00:00
#SBATCH --output=w_fcgenes.out
#SBATCH --error=w_fcgenes.err
#SBATCH --partition=gilad
#SBATCH --mem=8G
#SBATCH --mail-type=END


for i in $(ls /project2/gilad/briana/Net-seq-pilot/data/sort/*combined_Netpilot-sort.bam); do
            sbatch fc_gene.sh  $i 
        done

Genes with coverage per line

At this point 8 samples have over 100mil mapped reads. They are 18497, 18508, 18853, 18870, 19128, 19193, 19209 and 19239. We are waiting for more reads for 19225 and 18500. Unfortunately maping is low for 19223, but I have not diagnosed the problem yet.

Load libraries:

library(workflowr)
Loading required package: rmarkdown
This is workflowr version 1.0.1
Run ?workflowr for help getting started
library(dplyr)
Warning: package 'dplyr' was built under R version 3.4.4

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

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(ggplot2)
library(tidyr)
library(reshape2)
Warning: package 'reshape2' was built under R version 3.4.3

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(edgeR)
Warning: package 'edgeR' was built under R version 3.4.3
Loading required package: limma
Warning: package 'limma' was built under R version 3.4.3

Load data:

cov_18486=read.table("../data/fc_genecov/genecov.NET3-18486.txt", header=TRUE)
cov_18497=read.table("../data/fc_genecov/genecov.NET3-18497.txt", header=TRUE)
cov_18500=read.table("../data/fc_genecov/genecov.NET3-18500.txt", header=TRUE)
cov_18505=read.table("../data/fc_genecov/genecov.NET3-18505.txt", header=TRUE)
cov_18508=read.table("../data/fc_genecov/genecov.NET3-18508.txt", header=TRUE)
cov_18853=read.table("../data/fc_genecov/genecov.NET3-18853.txt", header=TRUE)
cov_18870=read.table("../data/fc_genecov/genecov.NET3-18870.txt", header=TRUE)
cov_19128=read.table("../data/fc_genecov/genecov.NET3-19128.txt", header=TRUE)
cov_19141=read.table("../data/fc_genecov/genecov.NET3-19141.txt", header=TRUE)
cov_19193=read.table("../data/fc_genecov/genecov.NET3-19193.txt", header=TRUE)
cov_19209=read.table("../data/fc_genecov/genecov.NET3-19209.txt", header=TRUE)
cov_19223=read.table("../data/fc_genecov/genecov.NET3-19223.txt", header=TRUE)
cov_19225=read.table("../data/fc_genecov/genecov.NET3-19225.txt", header=TRUE)
cov_19238=read.table("../data/fc_genecov/genecov.NET3-19238.txt", header=TRUE)
cov_19239=read.table("../data/fc_genecov/genecov.NET3-19239.txt", header=TRUE)
cov_19257=read.table("../data/fc_genecov/genecov.NET3-19257.txt", header=TRUE)

Process data

gene_length=cov_18486$End- cov_18486$Start

Standardize by gene length

cov_18486=cov_18486 %>% mutate(st_18486=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18486_combined_Netpilot.sort.bam/gene_length)
Warning: package 'bindrcpp' was built under R version 3.4.4
cov_18497=cov_18497 %>% mutate(st_18497=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18497_combined_Netpilot.sort.bam/gene_length)
cov_18500=cov_18500 %>% mutate(st_18500=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18500_combined_Netpilot.sort.bam/gene_length)
cov_18505=cov_18505 %>% mutate(st_18505=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18505_combined_Netpilot.sort.bam/gene_length)
cov_18508=cov_18508 %>% mutate(st_18508=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18508_combined_Netpilot.sort.bam/gene_length)
cov_18853=cov_18853 %>% mutate(st_18853=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18853_combined_Netpilot.sort.bam/gene_length)
cov_18870=cov_18870 %>% mutate(st_18870=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.18870_combined_Netpilot.sort.bam/gene_length)
cov_19128=cov_19128 %>% mutate(st_19128=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19128_combined_Netpilot.sort.bam/gene_length)
cov_19141=cov_19141 %>% mutate(st_19141=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19141_combined_Netpilot.sort.bam/gene_length)
cov_19193=cov_19193 %>% mutate(st_19193=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19193_combined_Netpilot.sort.bam/gene_length)
cov_19209=cov_19209 %>% mutate(st_19209=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19209_combined_Netpilot.sort.bam/gene_length)
cov_19223=cov_19223 %>% mutate(st_19223=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19223_combined_Netpilot.sort.bam/gene_length)
cov_19225=cov_19225 %>% mutate(st_19225=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19225_combined_Netpilot.sort.bam/gene_length)
cov_19238=cov_19238 %>% mutate(st_19238=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19238_combined_Netpilot.sort.bam/gene_length)
cov_19239=cov_19239 %>% mutate(st_19239=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19239_combined_Netpilot.sort.bam/gene_length)
cov_19257=cov_19257 %>% mutate(st_19257=X.project2.gilad.briana.Net.seq.pilot.data.sort.YG.SP.NET3.19257_combined_Netpilot.sort.bam/gene_length)

Join these on the gene name:

names=c("GeneID", "st_18486", "st_18497", "st_18500", "st_18505", "st_18508", "st_18853", "st_18870", "st_19128", "st_19141", "st_19193", "st_19209", "st_19223", "st_19225", "st_19238", "st_19239", "st_19257")
cov_all_df=data.frame(cov_18486$Geneid,cov_18486$st_18486, cov_18497$st_18497, cov_18500$st_18500, cov_18505$st_18505, cov_18508$st_18508, cov_18853$st_18853, cov_18870$st_18870, cov_19128$st_19128, cov_19141$st_19141, cov_19193$st_19193, cov_19209$st_19209, cov_19223$st_19223, cov_19225$st_19225, cov_19238$st_19238, cov_19239$st_19239, cov_19257$st_19257)

colnames(cov_all_df)= names

Genes with coverage

genes_detected=function(col, num){
  #takes incov_all_dfl col which corresponds to one library
  tot_genes=nrow(cov_all_df)
  exp_genes=sum(col >num)
  return(exp_genes/tot_genes)
}


detected_genes0=c(genes_detected(cov_all_df$st_18486, 0), genes_detected(cov_all_df$st_18497,0), genes_detected(cov_all_df$st_18500,0), genes_detected(cov_all_df$st_18505,0), genes_detected(cov_all_df$st_18508,0), genes_detected(cov_all_df$st_18853,0), genes_detected(cov_all_df$st_18870,0), genes_detected(cov_all_df$st_19128,0), genes_detected(cov_all_df$st_19141,0), genes_detected(cov_all_df$st_19193,0), genes_detected(cov_all_df$st_19209,0), genes_detected(cov_all_df$st_19223,0), genes_detected(cov_all_df$st_19225,0), genes_detected(cov_all_df$st_19238,0), genes_detected(cov_all_df$st_19239,0), genes_detected(cov_all_df$st_19257,0))

names(detected_genes0)=c("18486", "18497", "18500", "18505", "18508", "18853", "18870", "19128", "19141", "19193", "19209", "19223", "19225", "19238", "19239", "19257")

barplot(detected_genes0, ylim = c(0,1), main="Net-seq Genes detected greater than 0 standardized reads", ylab="Proportion non zero genes", xlab="Library", col = 'Blue')

abline(h=mean(detected_genes0))

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
9787ad5 Briana Mittleman 2018-06-22

0 is not the most informative detection rate because it could be due to noise. I need to look at the distribution to pick a better cuttoff.

plot(log10(sort(cov_all_df$st_18486, decreasing = T)))

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
f9622a0 Briana Mittleman 2018-06-22
9787ad5 Briana Mittleman 2018-06-22

I should use .001 or \(10^{-3}\) as a cuttoff.

detected_genes_cut=c(genes_detected(cov_all_df$st_18486, .001), genes_detected(cov_all_df$st_18497,.001), genes_detected(cov_all_df$st_18500,.001), genes_detected(cov_all_df$st_18505,.001), genes_detected(cov_all_df$st_18508,.001), genes_detected(cov_all_df$st_18853,.001), genes_detected(cov_all_df$st_18870,.001), genes_detected(cov_all_df$st_19128,0.001), genes_detected(cov_all_df$st_19141,0.001), genes_detected(cov_all_df$st_19193,0.001), genes_detected(cov_all_df$st_19209,0.001), genes_detected(cov_all_df$st_19223,0.001), genes_detected(cov_all_df$st_19225,0.001), genes_detected(cov_all_df$st_19238,0.001), genes_detected(cov_all_df$st_19239,0.001), genes_detected(cov_all_df$st_19257,0.001))

names(detected_genes_cut)=c("18486", "18497", "18500", "18505", "18508", "18853", "18870", "19128", "19141", "19193", "19209", "19223", "19225", "19238", "19239", "19257")

barplot(detected_genes_cut, ylim = c(0,1), main="Net-seq Genes detected greater than .001 standardized reads", ylab="Proportion genes passing filter", xlab="Library", col = 'Blue')

abline(h=mean(detected_genes_cut))

Expand here to see past versions of unnamed-chunk-11-1.png:
Version Author Date
f9622a0 Briana Mittleman 2018-06-22

Correlation between libraries:

cor_function=function(data){
  corr_matrix= matrix(0,ncol(data),ncol(data))
  for (i in seq(1,ncol(data))){
    for (j in seq(1,ncol(data))){
      x=cor.test(data[,i], data[,j], method='pearson')
      cor_ij=as.numeric(x$estimate)
      corr_matrix[i,j]=cor_ij
    }
  }
  return(corr_matrix)
}

covall_matrix=as.matrix(cov_all_df[,2:17])

covall_cor= cor_function(covall_matrix)
rownames(covall_cor)=c("NA18486", "NA18497", "NA18500", "NA18505", "NA18508", "NA18853", "NA18870", "NA19128", "NA19141", "NA19193", "NA19209", "NA19223", "NA19225", "NA19238", "NA19239", "NA19257")
colnames(covall_cor)=c("NA18486", "NA18497", "NA18500", "NA18505", "NA18508", "NA18853", "NA18870", "NA19128", "NA19141", "NA19193", "NA19209", "NA19223", "NA19225", "NA19238", "NA19239", "NA19257")


covall_cor_melt=melt(covall_cor)

ggheatmap=ggplot(data = covall_cor_melt, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile() +
  labs(title="Net-seq Correlation Heatplot")
ggheatmap

Expand here to see past versions of unnamed-chunk-12-1.png:
Version Author Date
f9622a0 Briana Mittleman 2018-06-22

Line 19223 is the line with mapping problems. I expected this one to have low correlations.

Look at Intergenic/genic bases

I will use the per_intergenic bases stat from the PICARD RNA metrics results.

genic_bases=read.csv("../data/netcomb_intronicbases.csv", header=T)

genic_bases=genic_bases %>% mutate(Pct_geneic=1-Pct_intergenic)


ggplot(genic_bases,aes(x=Library, y=Pct_geneic)) + geom_col(fill="blue") + labs(y="Pct mapped reads in genic region", title="Percent of mapped bases in genic region")

Session information

sessionInfo()
R version 3.4.2 (2017-09-28)
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/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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  edgeR_3.20.9    limma_3.34.9    reshape2_1.4.3 
[5] tidyr_0.7.2     ggplot2_2.2.1   dplyr_0.7.5     workflowr_1.0.1
[9] rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1.1      
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.15     lattice_0.20-35   evaluate_0.10.1  
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.1  
[16] rlang_0.2.1       yaml_2.1.19       stringr_1.3.1    
[19] knitr_1.18        locfit_1.5-9.1    rprojroot_1.3-2  
[22] grid_3.4.2        tidyselect_0.2.4  glue_1.2.0       
[25] R6_2.2.2          purrr_0.2.5       magrittr_1.5     
[28] whisker_0.3-2     backports_1.1.2   scales_0.5.0     
[31] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.3-2 
[34] labeling_0.3      stringi_1.2.2     lazyeval_0.2.1   
[37] munsell_0.4.3     R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.0.1