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  "Authors@R": "c(person(role=\"aut\", \"Bernd\", \"Klaus\", email = \"bernd.klaus@embl.de\"),\nperson(role=c(\"aut\", \"cre\"), \"Stefanie\", \"Reisenauer\", email = \"steffi.reisenauer@tum.de\"))",
  "Description": "In this article, we walk through an end-to-end Affymetrix\nmicroarray differential expression workflow using Bioconductor\npackages. This workflow is directly applicable to current\n\"Gene\" type arrays, e.g. the HuGene or MoGene arrays, but can\neasily be adapted to similar platforms. The data analyzed here\nis a typical clinical microarray data set that compares\ninflamed and non-inflamed colon tissue in two disease subtypes.\nFor each disease, the differential gene expression between\ninflamed- and non-inflamed colon tissue was analyzed. We will\nstart from the raw data CEL files, show how to import them into\na Bioconductor ExpressionSet, perform quality control and\nnormalization and finally differential gene expression (DE)\nanalysis, followed by some enrichment analysis.",
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