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Full-Text Articles in Computational Biology
Mixture Models For Assessing Differential Expression In Complex Tissues Using Microarray Data, Debashis Ghosh
Mixture Models For Assessing Differential Expression In Complex Tissues Using Microarray Data, Debashis Ghosh
The University of Michigan Department of Biostatistics Working Paper Series
The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous. In this article, we outline a general framework for determining …
Cluster Stability Scores For Microarray Data In Cancer Studies, Mark Smolkin, Debashis Ghosh
Cluster Stability Scores For Microarray Data In Cancer Studies, Mark Smolkin, Debashis Ghosh
The University of Michigan Department of Biostatistics Working Paper Series
A potential benefit of profiling of tissue samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Hierarchical clustering has been the primary analytical tool used to define disease subtypes from microarray experiments in cancer settings. Assessing cluster reliability poses a major complication in analyzing output from these procedures. While much work has been done on assessing the global question of number of clusters in a dataset, relatively little research exists on assessing stability of individual clusters. A potential benefit of profiling of tissue samples using microarrays is the generation of molecular fingerprints that will …