Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models

M. Seifert, M. Strickert, A. Schliep and I. Grosse

Bioinformatics 2011, 27:12, 1645–1652.

MOTIVATION: Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. RESULTS: We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. We train the DSHMM by integrating prior knowledge about potential distributions of expression levels of differentially expressed and unchanged genes in tumor. We find that especially the combination of this data and to a lesser extent the modeling of distances between adjacent genes contribute to a substantial improvement of the identification of differentially expressed genes in comparison to other existing methods. This performance benefit is also supported by the identification of genes well-known to be associated with breast cancer. That suggests applications of DSHMMs for screening of other tumor expression profiles.

Pubmed ID: 21511716. DOI: 10.1093/bioinformatics/btr199.

The publication includes results from the following projects or software tools: ArrayCGH.

Further publications by Alexander Schliep, Michael Seifert.