Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables.

ABSTRACT: A fundamental step in the analysis of gene expression and other high-dimensional genomic data is the calculation of the similarity or distance between pairs of individual samples in a study. If one has collected N total samples and assayed the expression level of G genes on those samples, then an N x N similarity matrix can be formed that reflects the correlation or similarity of the samples with respect to the expression values over the G genes. This matrix can then be examined for patterns via standard data reduction and cluster analysis techniques. We consider an alternative to conventional data reduction and cluster analyses of similarity matrices that is rooted in traditional linear models. This analysis method allows predictor variables collected on the samples to be related to varia

SUBMITTER: Zapala MA 

PROVIDER: S-EPMC1748243 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature