Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as parameters which are compositions of individual mappings for clustering patients and genes. This framework allows one to assess classical properties of clustering methods, such as consistency, and to formally study statistical inference regarding the clustering parameter. We present results of simulations designed to assess the asymptotic validity of different bootstrap methods for estimating the distributions of estimated simultaneous clustering parameters. The method is illustrated on a publicly available data set.
Bioinformatics | Computational Biology | Microarrays | Multivariate Analysis | Statistical Methodology | Statistical Theory
Pollard, Katherine S. and van der Laan, Mark J., "Statistical Inference for Simultaneous Clustering of Gene Expression Data" (July 2001). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 96.