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使用Affymetrix微阵列进行差异基因表达分析的端到端工作流程。

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

作者信息

Klaus Bernd, Reisenauer Stefanie

机构信息

EMBL Heidelberg, Heidelberg, 69117, Germany.

出版信息

F1000Res. 2016 Jun 15;5:1384. doi: 10.12688/f1000research.8967.2. eCollection 2016.

DOI:10.12688/f1000research.8967.2
PMID:30101006
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6063319/
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.

摘要

在本文中，我们将使用Bioconductor软件包详细介绍一个端到端的Affymetrix微阵列差异表达工作流程。此工作流程直接适用于当前的“基因”类型阵列，例如HuGene或MoGene阵列，但也可轻松适配类似平台。这里分析的数据是一个典型的临床微阵列数据集，用于比较两种疾病亚型中发炎和未发炎的结肠组织。对于每种疾病，分析了发炎和未发炎结肠组织之间的差异基因表达。我们将从原始数据CEL文件开始，展示如何将其导入Bioconductor ExpressionSet，进行质量控制和标准化，最后进行差异基因表达（DE）分析，随后进行一些富集分析。

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