Matrix factorisation methods applied in microarray data analysis

Authors:
Andrew V. Kossenkov;Michael F. Ochs
Affiliations:
The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104, USA.;Department of Oncology, Johns Hopkins University, 550 North Broadway, Suite 1103, Baltimore, MD 21205, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

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Abstract

Numerous methods have been applied to microarray data to group genes into clusters that show similar expression patterns. These methods assign each gene to a single group, which does not reflect the widely held view among biologists that most, if not all, genes in eukaryotes are involved in multiple biological processes and therefore will be multiply regulated. Here, we review several methods of matrix factorisation that identify patterns of behaviour in transcriptional response and assign genes to multiple patterns. We focus on these methods rather than traditional clustering methods applied to microarray data, which assign one gene to one cluster.