STEM Short Time-series Expression Miner (v1.1) User Manual

6 Comparison 30 7 K-means 35 A Defaults File Format 40 B Using STEM for Standard Gene Ontology Enrichment Analysis 42 C Gene Annotation Sources 43 ii 1 Introduction Welcome to STEM! STEM is an acronym for the Short Time-series Expression Miner, a software program designed for clustering, comparing, and visualizing gene expression data from short time series microarray experiments (∼8 time points or fewer). STEM implements a novel method for clustering short time series expression data that can differentiate between real and random patterns. STEM is also integrated with the Gene Ontology (GO) [4] allowing efficient biological interpretations of the data. The novel clustering method that STEM implements first defines a set of distinct and representative model temporal expression profiles independent of the data. These model profiles correspond to possible profiles of a gene's change in expression over time. The model profiles all start at 0, and then between two time points a model profile can either hold steady or increase or decrease an integral number of time units up to a parameter value. Gene expression times series are transformed to start at 0, and each gene is assigned to the model profile to which its time series most closely matches based on the correlation coefficient. The number of genes assigned to each model profile is then computed. The number of genes expected to be assigned to a profile is estimated by randomly permuting the original time point values, renormalizing the gene's expression values, then assigning genes to their most closely matching model profiles, and repeating for a large number of permutations. The average number of genes assigned to a model profile over all permutations is used as the estimate of the expected number of genes assigned to the profile. The statistical significance of the number of genes assigned to each profile versus the number expected is also then computed. Statistically significant model profiles which are similar to each other can be grouped together to form clusters of profiles. The biological significance of the set of genes assigned to the same profile or the same cluster of profiles can then be assessed using a GO enrichment analysis. For a more detailed discussion of the novel method STEM uses to cluster genes and associate statistical significance with genes having the same expression profile see [3]. The remainder of the main portion of the manual contains six sections. Section 2 contains …