Model-based cluster analysis of microarray gene-expression data - PubMed (original) (raw)
Model-based cluster analysis of microarray gene-expression data
Wei Pan et al. Genome Biol. 2002.
Abstract
Background: Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic.
Results: The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels.
Conclusions: Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.
Figures
Figure 1
Histograms of radioactivity intensity levels for the first experiment, a cDNA microarray analysis of 1,176 genes in middle-ear mucosa of healthy (control) rats. (a) Before log-transformation; (b) after log-transformation.
Figure 2
Comparison of the log-transformed, standardized expression data between experiments. Experiments 1 and 2 were conducted using control rats; experiments 3-6 used infected rats.
Figure 3
Gene-expression profiles of the four clusters found using the method described. Each line represents a single gene. Clusters 2 and 3 (containing over 95% of genes) show little change in gene-expression levels; cluster 1 (30 genes) and cluster 4 (6 genes) do show changes in gene-expression levels.
Figure 4
Posterior probability of a gene being in each cluster as a function of the _t_-statistic y, calculated using Equations (1) and (2). A gene is classified to a cluster if its posterior probability of being in the cluster is the largest.
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