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Reliable and extensive analysis of transcriptomics data with package `VisomX`

Nicolas T. Wirth

2022-01-31

Source: vignettes/vignette_transcriptomics.Rmd
vignette_transcriptomics.Rmd

Introduction

The aim of the VisomX’s transcriptomics section is to provide an integrated analysis workflow for robust and reproducible analysis of RNAseq data for differential gene expression. This section is built on the foundation of the R package DESeq2 (Love et al., 2014). VisomX was designed to be user-friendly, requiring minimal prior knowledge of the R programming language or programming in general. However, a basic understanding of running commands from a console or writing simple scripts is necessary. For those new to R, this website is a great starting point, there are some good resources here and I suggest using the RStudio application. It provides an environment for writing and running R code.

With consideration for R novices, VisomX establishes a framework in which a complete, detailed RNASeq data analysis can be performed in two simple steps:

  1. Read data from one or several tabular inputs with gene counts, assign new names and apply feature filtering.

  2. Run workflow, including missing value imputation, normalization, log2 transformation, statistical analysis, differential expression analysis, log2 fold change shrinkage, pathway enrichment analysis, and rendering of a report that summarizes the results.

All computational results of a workflow are stored in a data container (list) and can be visualized by passing them to a set of dedicated plotting functions.

Installation

Install the most current version with package devtools:

install.packages("devtools")
library(devtools)

install_github("NicWir/VisomX", dependencies = TRUE)

Load the package:

# Load the VisomX package
library(VisomX)

References

Love M.I., Huber W. & Anders S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15 (12): 550. https://doi.org/10.1186/s13059-014-0550-8.