M. Kathleen Kerr

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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. While the power of this technology has been recognized, many open questions remain about(More)
ii ABSTRACT We describe a software package called MAANOVA, for MicroArray ANalysis Of VAriance. MAANOVA is a collection of functions for statistical analysis of gene expression data from two-color cDNA microarray experiments. It is available in both the Matlab and R programming environments and can be run on any platform that supports these packages.(More)
We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization(More)
Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue(More)
Two channel microarray data often contain systematic variations that can be minimized by data transformation prior to further analysis. The most commonly observed effects are revealed by viewing scatter plots of the logarithm of the ratio by the average logarithmic intensity of the two color channels (RI plots). In this paper we present a general model for(More)
Common ratio-based approaches for analyzing gene expression microarray data do not provide a framework for handling replication, although replication is clearly desirable for these noisy data. In contrast, replication fits naturally into analysis of variance (ANOVA) methods. We use ANOVA to analyze data from a microarray experiment to compare gene(More)
In the past several years many linear models have been proposed for analyzing two-color microarray data. As presented in the literature, many of these models appear dramatically different. However, many of these models are reformulations of the same basic approach to analyzing microarray data. This paper demonstrates the equivalence of some of these models.(More)
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