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. 2016 Jun 15:5:1384.
doi: 10.12688/f1000research.8967.2. eCollection 2016.

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

Bernd Klaus  1 , Stefanie Reisenauer  1
Affiliations

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

Bernd Klaus et al. F1000Res. .

Abstract

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.

Keywords: gene expression; microarray.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Structure of Bioconductor’s ExpressionSet class.
Figure 2.
Figure 2.. PCA plot of the log–transformed raw expression data.
Figure 3.
Figure 3.. Intensity boxplots of the log2–transformed raw data.
Figure 4.
Figure 4.. Visualization of the difference between "Exon" type array (left) and "Gene" type array (right).
Figure 5.
Figure 5.. Boxplot for the RLE values.
Figure 6.
Figure 6.. PCA plot of the calibrated, summarized data.
Figure 7.
Figure 7.. Heatmap of the sample-to-sample distances.
Figure 8.
Figure 8.. Histogram of the median intensities per gene.
Figure 9.
Figure 9.. Histogram of the median intensities per gene with manual intensity filtering threshold (red line).
Figure 10.
Figure 10.. Visualization of expression changes.
Figure 11.
Figure 11.. Expression changes for the CRAT gene.
Figure 12.
Figure 12.. Histogram of the p–values for Crohn’s disease.
Figure 13.
Figure 13.. Histogram of the p–values for ulcerative colitis.
Figure 14.
Figure 14.. Volcano plot of the DE-genes.
Figure 15.
Figure 15.. Selecting a background set of genes for the gene ontology analysis.
Figure 16.
Figure 16.. Significantly enriched GO nodes in the GO hierarchy.
Figure 17.
Figure 17.. Enriched Reactome pathways and their p–values as a bar chart.
Figure 18.
Figure 18.. Enriched Reactome pathways enrichment results as a graph.
See this image and copyright information in PMC

References

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