An end to end workflow for differential gene expression using
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چکیده
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 adapted to similar platforms. The data re–analyzed is a typical clinical microarray data set that compares inflammed and non–inflammed colon tissue in two disease subtypes. 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. As experimental designs can be complex, a self contained introduction to linear models is also part of the workflow. This article is included in the Bioconductor gateway. Bernd Klaus ( ) Corresponding author: [email protected] The author declares that there are no competing interests. Competing interests: Klaus B. How to cite this article: An end to end workflow for differential gene expression using Affymetrix microarrays [version 1; 2016, :1384 (doi: ) referees: 1 approved, 1 approved with reservations] F1000Research 5 10.12688/f1000research.8967.1 © 2016 Klaus B. This is an open access article distributed under the terms of the , which permits Copyright: Creative Commons Attribution Licence unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the (CC0 1.0 Public domain dedication). Creative Commons Zero "No rights reserved" data waiver The author(s) declared that no grants were involved in supporting this work. Grant information: 15 Jun 2016, :1384 (doi: ) First published: 5 10.12688/f1000research.8967.1 Referee Status:
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An end to end workflow for differential gene expression using
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 adapted to similar platforms. The data re–analyzed is a typical clinical microarray data set that compares inflammed and non–inflammed colon tissue in...
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