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maEndToEnd

An end to end workflow for differential gene expression using Affymetrix microarrays

Bioconductor version: 3.24 · Package version: 2.33.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("maEndToEnd")

Details

MaintainerStefanie Reisenauer <steffi.reisenauer@tum.de>
AuthorBernd Klaus [aut], Stefanie Reisenauer [aut, cre]
LicenseMIT + file LICENSE
URLhttps://www.bioconductor.org/help/workflows/
Downloads rank114
Source branchdevel
biocViewsGeneExpressionWorkflow, Workflow

Download

Follow the installation instructions to use this package in your R session.

Source packagemaEndToEnd_2.33.0.tar.gz
Dependencies

Depends: R (>= 3.5.0), Biobase, oligoClasses, ArrayExpress, pd.hugene.1.0.st.v1, hugene10sttranscriptcluster.db, oligo, arrayQualityMetrics, limma, topGO, ReactomePA, clusterProfiler, gplots, ggplot2, geneplotter, pheatmap, RColorBrewer, dplyr, tidyr, stringr, matrixStats, genefilter, openxlsx, Rgraphviz, enrichplot

Suggests: BiocStyle, knitr, devtools, rmarkdown