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File Version Author Date Message
html 96ecbb1 Briana Mittleman 2018-05-26 Build site.
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)
This is workflowr version 1.2.0
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)
Loading required package: limma

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")

Version Author Date
96ecbb1 Briana Mittleman 2018-05-26

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")

Version Author Date
96ecbb1 Briana Mittleman 2018-05-26

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")

Version Author Date
96ecbb1 Briana Mittleman 2018-05-26

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")

Version Author Date
96ecbb1 Briana Mittleman 2018-05-26
Post filtering we are left with 12461 protein coding genes.


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  edgeR_3.22.5    limma_3.36.5    dplyr_0.7.6    
[5] ggplot2_3.0.0   workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     compiler_3.5.1   pillar_1.3.0     git2r_0.24.0    
 [5] plyr_1.8.4       bindr_0.1.1      tools_3.5.1      digest_0.6.17   
 [9] lattice_0.20-35  evaluate_0.13    tibble_1.4.2     gtable_0.2.0    
[13] pkgconfig_2.0.2  rlang_0.2.2      yaml_2.2.0       withr_2.1.2     
[17] stringr_1.4.0    knitr_1.20       fs_1.2.6         locfit_1.5-9.1  
[21] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.4 glue_1.3.0      
[25] R6_2.3.0         rmarkdown_1.11   purrr_0.2.5      magrittr_1.5    
[29] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[33] assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4    lazyeval_0.2.1  
[37] munsell_0.5.0    crayon_1.3.4