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Physical Sciences and Mathematics
The University of Michigan Department of Biostatistics Working Paper Series
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Full-Text Articles in Genetics and Genomics
Finding Cancer Subtypes In Microarray Data Using Random Projections, Debashis Ghosh
Finding Cancer Subtypes In Microarray Data Using Random Projections, Debashis Ghosh
The University of Michigan Department of Biostatistics Working Paper Series
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 …
Semiparametric Methods For Identification Of Tumor Progression Genes From Microarray Data, Debashis Ghosh, Arul Chinnaiyan
Semiparametric Methods For Identification Of Tumor Progression Genes From Microarray Data, Debashis Ghosh, Arul Chinnaiyan
The University of Michigan Department of Biostatistics Working Paper Series
The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression. In this article, we develop statistical procedures for the identification of such genes, which we term tumor progression genes. Two methods are considered in this paper. The first is use of a proportional odds procedure, combined with false discovery rate estimation techniques to adjust for the multiple testing problem. The second method is based on order-restricted estimation …