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Exploring Gene Expression Data with Class Scores

By Paul Pavlidis, Darrin P. Lewis and William Stafford Noble


We ad(h'ess a commonly asked question about gene expression data sets: "What functional classes of genes are most interesting in the data?" In the methods we present, expression data is partitioned into classes based on existing annotation schemes. Each class is then given three separately derived "interest" scores. The fu'st score is based on an assessment of the statistical significance of gene expression changes experienced by members of the class, in the context of the experimental design

Year: 2002
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