Gene Selection and clustering for time-course and dose-response microarray experiments using order-restricted inference

Biostatistics Branch, Laboratory of Molecular Carcinogenesis, Research Triangle Park, NC 27709, USA.
Bioinformatics (Impact Factor: 4.98). 06/2003; 19(7):834-41. DOI: 10.1093/bioinformatics/btg093
Source: PubMed


We propose an algorithm for selecting and clustering genes according to their time-course or dose-response profiles using gene expression data. The proposed algorithm is based on the order-restricted inference methodology developed in statistics. We describe the methodology for time-course experiments although it is applicable to any ordered set of treatments. Candidate temporal profiles are defined in terms of inequalities among mean expression levels at the time points. The proposed algorithm selects genes when they meet a bootstrap-based criterion for statistical significance and assigns each selected gene to the best fitting candidate profile. We illustrate the methodology using data from a cDNA microarray experiment in which a breast cancer cell line was stimulated with estrogen for different time intervals. In this example, our method was able to identify several biologically interesting genes that previous analyses failed to reveal.

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Available from: Shyamal Peddada
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    • "For hierarchical clustering, the data were standardized to mean  = 0, variance  = 1 and grouped using Euclidean distance as the dissimilarity measure and average linkage for merging. Since the data are not necessarily normally distributed with equal variances, and since the sample sizes are unequal among the comparison groups, we performed all comparisons using standard residual bootstrap methodology [39] implemented in ORIOGEN v.3.0 which is based on [40], [41] using 100000 bootstrap samples with a SAM correction of 0.10. Specifically, we compared samples from normal and tumor tissues, tumor tissues from older black and older whites, and tumor tissues from older and younger black women. "
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    • "Although we do not discuss the problem of selecting significant gene sets and subsets when comparing multiple experimental conditions, the proposed methodology can be extended to such situations by replacing Hotelling’s T2 statistic by commonly used statistics such as the Hotelling-Lawley trace test or the Roy’s largest root test. Furthermore, if the experimental conditions are ordered, such as in a time-course or a dose-response study, one can exploit order-restricted inference based methods developed in [29]. As commented by a reviewer of this manuscript, it is possible that in some applications only a few genes in a given pathway are differentially expressed where such subsets are not necessarily pre-defined. "
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