Last updated: 2018-05-26

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    Rmd 2076ce9 Briana Mittleman 2018-05-26 initial commit, gene level analysis

I will use this analysis to take a look at the initial protein conding gene counts.

library(workflowr)
Loading required package: rmarkdown
This is workflowr version 1.0.1
Run ?workflowr for help getting started
library(ggplot2)
library(dplyr)

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(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

Imput the data that was created from my coding gene rule in the snakefile.

N_18486_cov= read.table("../data/gene_cov/YL-SP-18486-N_S10_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))

T_18486_cov= read.table("../data/gene_cov/YL-SP-18486-T_S9_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18497_cov= read.table("../data/gene_cov/YL-SP-18497-N_S12_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18497_cov= read.table("../data/gene_cov/YL-SP-18497-T_S11_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18500_cov= read.table("../data/gene_cov/YL-SP-18500-N_S20_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18500_cov= read.table("../data/gene_cov/YL-SP-18500-T_S19_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18505_cov= read.table("../data/gene_cov/YL-SP-18505-N_S2_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18505_cov= read.table("../data/gene_cov/YL-SP-18505-T_S1_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18508_cov= read.table("../data/gene_cov/YL-SP-18508-N_S6_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18508_cov= read.table("../data/gene_cov/YL-SP-18508-T_S5_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18853_cov= read.table("../data/gene_cov/YL-SP-18853-N_S32_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18853_cov= read.table("../data/gene_cov/YL-SP-18853-T_S31_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_18870_cov= read.table("../data/gene_cov/YL-SP-18870-N_S24_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_18870_cov= read.table("../data/gene_cov/YL-SP-18870-T_S23_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19128_cov= read.table("../data/gene_cov/YL-SP-19128-N_S30_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19128_cov= read.table("../data/gene_cov/YL-SP-19128-T_S29_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19141_cov= read.table("../data/gene_cov/YL-SP-19141-N_S18_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19141_cov= read.table("../data/gene_cov/YL-SP-19141-T_S17_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19193_cov= read.table("../data/gene_cov/YL-SP-19193-N_S22_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19193_cov= read.table("../data/gene_cov/YL-SP-19193-T_S21_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19209_cov= read.table("../data/gene_cov/YL-SP-19209-N_S16_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19209_cov= read.table("../data/gene_cov/YL-SP-19209-T_S15_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19223_cov= read.table("../data/gene_cov/YL-SP-19223-N_S8_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19223_cov= read.table("../data/gene_cov/YL-SP-19233-T_S7_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19225_cov= read.table("../data/gene_cov/YL-SP-19225-N_S28_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19225_cov= read.table("../data/gene_cov/YL-SP-19225-T_S27_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19238_cov= read.table("../data/gene_cov/YL-SP-19238-N_S4_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19238_cov= read.table("../data/gene_cov/YL-SP-19238-T_S3_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19239_cov= read.table("../data/gene_cov/YL-SP-19239-N_S14_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19239_cov= read.table("../data/gene_cov/YL-SP-19239-T_S13_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
N_19257_cov= read.table("../data/gene_cov/YL-SP-19257-N_S26_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))
T_19257_cov= read.table("../data/gene_cov/YL-SP-19257-T_S25_R1_001-genecov.txt", stringsAsFactors = FALSE, header = F, col.names = c("chr", "start", "end", "gene", "score", "strand", "count" ))

Look at the total libraries first:

total_count_matrix=cbind(T_18486_cov$count, T_18497_cov$count, T_18500_cov$count, T_18505_cov$count, T_18508_cov$count, T_18853_cov$count, T_18870_cov$count, T_19128_cov$count, T_19141_cov$count, T_19193_cov$count, T_19209_cov$count, T_19223_cov$count, T_19225_cov$count, T_19238_cov$count,T_19239_cov$count, T_19257_cov$count)

#gene length vector
gene_length=T_18497_cov %>% mutate(genelength=end-start) %>% select(genelength) 
gene_length_vec=as.vector(gene_length$genelength)
total_count_matrix_cpm=cpm(total_count_matrix, log=T, gene.length=gene_length_vec )

Plot distribution of log2 cpm for total libraries.

plotDensities(total_count_matrix_cpm, legend = "bottomright", main="Pre-filtering total fraction")

Look at gene distributions for the nuclear fractions.

nuclear_count_matrix=cbind(N_18486_cov$count, N_18497_cov$count, N_18500_cov$count, N_18505_cov$count, N_18508_cov$count, N_18853_cov$count, N_18870_cov$count, N_19128_cov$count, N_19141_cov$count, N_19193_cov$count, N_19209_cov$count, N_19223_cov$count, N_19225_cov$count, N_19238_cov$count,N_19239_cov$count, N_19257_cov$count)

#cpm  

nuclear_count_matrix_cpm=cpm(nuclear_count_matrix, log=T, gene.length=gene_length_vec )

Plot distribution of log2 cpm for nuclear libraries.

plotDensities(nuclear_count_matrix_cpm, legend = "bottomright", main="Pre-filtering nuclear fraction")

The distributions look similar. I can filter based on alll of the libraries. I will filter for 1cpm in more than half of the libraries. After this I can ask how many genes are detected in each library.

all_count_matrix=cbind(T_18486_cov$count, T_18497_cov$count, T_18500_cov$count, T_18505_cov$count, T_18508_cov$count, T_18853_cov$count, T_18870_cov$count, T_19128_cov$count, T_19141_cov$count, T_19193_cov$count, T_19209_cov$count, T_19223_cov$count, T_19225_cov$count, T_19238_cov$count,T_19239_cov$count, T_19257_cov$count,N_18486_cov$count, N_18497_cov$count, N_18500_cov$count, N_18505_cov$count, N_18508_cov$count, N_18853_cov$count, N_18870_cov$count, N_19128_cov$count, N_19141_cov$count, N_19193_cov$count, N_19209_cov$count, N_19223_cov$count, N_19225_cov$count, N_19238_cov$count,N_19239_cov$count, N_19257_cov$count )


#cpm  

all_count_matrix_cpm=cpm(all_count_matrix, log=T, gene.length=gene_length_vec )
plotDensities(all_count_matrix_cpm, legend = "bottomright", main="Pre-filtering all libraries")

Filter:

keep.exprs=rowSums(all_count_matrix_cpm>1) >= 16
all_count_matrix_cpm_filt= all_count_matrix_cpm[keep.exprs,]

plotDensities(all_count_matrix_cpm_filt, legend = "bottomright", main="Post-filtering all libraries")

Post filtering we are left with 12461 protein coding genes.

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    edgeR_3.20.9    limma_3.34.9    dplyr_0.7.4    
[5] ggplot2_2.2.1   workflowr_1.0.1 rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1        
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.14     evaluate_0.10.1   tibble_1.4.2     
[13] gtable_0.2.0      lattice_0.20-35   pkgconfig_2.0.1  
[16] rlang_0.1.6       yaml_2.1.16       stringr_1.2.0    
[19] knitr_1.18        locfit_1.5-9.1    rprojroot_1.3-2  
[22] grid_3.4.2        glue_1.2.0        R6_2.2.2         
[25] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
[28] scales_0.5.0      htmltools_0.3.6   assertthat_0.2.0 
[31] colorspace_1.3-2  stringi_1.1.6     lazyeval_0.2.1   
[34] munsell_0.4.3     R.oo_1.22.0

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