Finding cancer subtypes in microarray data using random projections

Debashis Ghosh
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Debashis Ghosh: University of Michigan

No 1045, The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: One of the benefits of profiling of cancer samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Such subgroups have typically been found in microarray data using hierarchical clustering. A major problem in interpretation of the output is determining the number of clusters. We approach the problem of determining disease subtypes using mixture models. A novel estimation procedure of the parameters in the mixture model is developed based on a combination of random projections and the expectation-maximization algorithm. Because the approach is probabilistic, our approach provides a measure for the number of true clusters in a given dataset. We illustrate our approach with applications to both simulated and real microarray data.

Keywords: expectation-maximization algorithm; gene expression data; high-dimensional data; mixture models (search for similar items in EconPapers)
Date: 2004-10-20
Note: oai:bepress.com:umichbiostat-1045
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