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File Version Author Date Message
html cfc8f43 Briana Mittleman 2018-07-13 Build site.
Rmd 4a84769 Briana Mittleman 2018-07-13 add analysis for coparing coverage of RNAseq and 3’ seq

I will use this analysis to compare the 3’ seq data to the RNA seq data. I am going to look at the protein coding genes.

library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(ggplot2)
library(tidyr)
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(reshape2)

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

    smiths

Load RNA seq gene cov for 18486.

rnaseq=read.table("../data/18486.genecov.txt")
names(rnaseq)=c("Chr", "start", "end", "gene", "score", "strand", "count")
rnaseq_counts= rnaseq %>% select(gene, count)

Load all total fraction 3’ seq libraries.

t18486=read.table("../data/gene_cov/YL-SP-18486-T_S9_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18486") )
t18497=read.table("../data/gene_cov/YL-SP-18497-T_S11_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18497") )
t18500=read.table("../data/gene_cov/YL-SP-18500-T_S19_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18500") )
t18505=read.table("../data/gene_cov/YL-SP-18500-T_S19_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18505") )
t18508=read.table("../data/gene_cov/YL-SP-18508-T_S5_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18508") )
t18853=read.table("../data/gene_cov/YL-SP-18853-T_S31_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18853") )
t18870=read.table("../data/gene_cov/YL-SP-18870-T_S23_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T18870") )
t19128=read.table("../data/gene_cov/YL-SP-19128-T_S29_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19128") )
t19141=read.table("../data/gene_cov/YL-SP-19141-T_S17_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19141") )
t19193=read.table("../data/gene_cov/YL-SP-19193-T_S21_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19193") )
t19209=read.table("../data/gene_cov/YL-SP-19209-T_S15_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19209") )
t19223=read.table("../data/gene_cov/YL-SP-19233-T_S7_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19223") )
t19225=read.table("../data/gene_cov/YL-SP-19225-T_S27_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19225") )
t19238=read.table("../data/gene_cov/YL-SP-19238-T_S3_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19238") )
t19239=read.table("../data/gene_cov/YL-SP-19239-T_S13_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19239") )
t19257=read.table("../data/gene_cov/YL-SP-19257-T_S25_R1_001-genecov.txt",col.names =c("Chr", "start", "end", "gene", "score", "strand", "T19257") )

Merge all of the files:

threeprimeall=cbind(t18486,t18497$T18497, t18500$T18500, t18505$T18505, t18508$T18508, t18853$T18853, t18870$T18870, t19128$T19128, t19141$T19141,t19193$T19193, t19209$T19209, t19223$T19223, t19225$T19225, t19238$T19238, t19239$T19239, t19257$T19257)


threeprimeall_sum=threeprimeall %>% mutate(Counts_all= T18486,t18497$T18497, t18500$T18500, t18505$T18505, t18508$T18508, t18853$T18853, t18870$T18870, t19128$T19128, t19141$T19141,t19193$T19193, t19209$T19209, t19223$T19223, t19225$T19225, t19238$T19238, t19239$T19239, t19257$T19257) %>% select(gene, Counts_all)
threeprimeall_sum$gene=as.character(threeprimeall_sum$gene)

Melt the data fro ggplot.

all_counts= cbind(threeprimeall_sum,rnaseq_counts$count) 
colnames(all_counts)= c("gene", "threeprime", "RNAseq")

all_counts_melt= melt(all_counts, id.vars="gene")
names(all_counts_melt)=c("gene", "Library", "Count")

Plot the CDFs

ggplot(all_counts_melt, aes(x=log10(Count), col=Library)) + stat_ecdf(geom = "step", pad = FALSE) + labs(title= "Log10 counts CDF")
Warning: Removed 9859 rows containing non-finite values (stat_ecdf).

Version Author Date
cfc8f43 Briana Mittleman 2018-07-13 


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  reshape2_1.4.3  dplyr_0.7.6     tidyr_0.8.1    
[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] evaluate_0.13    tibble_1.4.2     gtable_0.2.0     pkgconfig_2.0.2 
[13] rlang_0.2.2      yaml_2.2.0       withr_2.1.2      stringr_1.4.0   
[17] knitr_1.20       fs_1.2.6         rprojroot_1.3-2  grid_3.5.1      
[21] tidyselect_0.2.4 glue_1.3.0       R6_2.3.0         rmarkdown_1.11  
[25] purrr_0.2.5      magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[29] scales_1.0.0     htmltools_0.3.6  assertthat_0.2.0 colorspace_1.3-2
[33] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[37] crayon_1.3.4