Gene Expression Arrays
Overview
Analysis of variance for expression data
Experimental design for microarrays
Bootstrapping cluster Analysis
Design and analysis of microarrays
Statistical analysis of a gene expression microarray experiment with replication
Analysis of a designed microarray experiment
Analysis of variance for gene expression microarray data
M. Kathleen Kerr, Mitchell Martin, and Gary Churchill
Abstract:Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. As the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent ``noise'' in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data. Manuscript
