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Briefings in Bioinformatics Advance Access first published online on September 29, 2007
This version published online on October 17, 2007
Briefings in Bioinformatics, doi:10.1093/bib/bbm043
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© Oxford University Press, 2007, All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

W. Gregory Alvord, Jean A. Roayaei, Octavio A. Quiñones and Katherine T. Schneider

Corresponding author. W. Gregory Alvord, PhD, Director, Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. Tel: 301.846.5101; Fax: 301.846.6196; E-mail: gwa{at}css.ncifcrf.gov

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip® soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.

Keywords: microarray, Affymetrix GeneChip®, probe-level data preprocessing, quality control, differential gene expression analysis

The figures have been updated

Submitted: May 1, 2007. Accepted: August 31, 2007.


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