Here is How RNAseq.work Works

The R package “rnaseq.work” helps you run differential expression analysis of RNAseq data using different packages through a single command (“rna_diff_expr”). Also, you can make different types of plots using another single command (“rna_visualize”).

Install the package from github using devtools. Here are the commands -

library("devtools")
install_github("homologus/rnaseq.work")
library("rnaseq.work")

Also make sure you have tidyverse, DESeq, limma and edgeR installed.

We are using the Pasilla data to demonstrate the functions. Load Pasilla data in ‘count_table’ data frame.

library("pasilla")
datafile=system.file("extdata/pasilla_gene_counts.tsv",package="pasilla")
count_table = read.table(datafile,header=TRUE,row.names=1,quote="",comment.char="")

Additionally create a ‘design_table’ data frame in the following manner. The condition column will change based on how your data set is.

names=c("untreated1","untreated2","untreated3","untreated4","treated1","treated2","treated3")
condition=c("untreated","untreated","untreated","untreated","treated","treated","treated")
design_table=data.frame(names,condition)

Once you have those two tables, the analysis is easy.

edgeR -

result_edgeR = rna_diff_expr(count_table, design_table, method="edgeR")

DESeq -

result_DESeq = rna_diff_expr(count_table, design_table, method="DESeq")

DESeq2 -

result_DESeq2 = rna_diff_expr(count_table, design_table, method="DESeq2")

Limma-voom -

result_limma = rna_diff_expr(count_table, design_table, method="limma-voom")

The visualization function works in the following manner. You call “rna_visualize” with different values of “method”. For example, to plot biological coefficient of variation, try (method=”BCV”) -

rna_visualize(count_table,design_table=design_table,method="BCV")

Try (method=”PCA”) to plot PCA -

rna_visualize(count_table, design_table=design_table, method="PCA")

We implemented a number of other options for rna_visualize. They will be explained in the manual.