This is the development version of maEndToEnd; for the stable release version, see maEndToEnd.
An end to end workflow for differential gene expression using Affymetrix microarraysBioconductor version: Development (3.22)
In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene" type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis.
Author: Bernd Klaus [aut], Stefanie Reisenauer [aut, cre]
Maintainer: Stefanie Reisenauer <steffi.reisenauer at tum.de>
Citation (from within R, entercitation("maEndToEnd")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("maEndToEnd")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("maEndToEnd")Follow Installation instructions to use this package in your R session.
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