2004/02/29 by David R. Bickel, Bickel, David R.
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Mathematics · #62-07 #62P10 #FOS: Biological sciences #Gene expression and cancer classification #Molecular Networks (q-bio.MN) #Optimal Experimental Design Methods #Quantitative Methods (q-bio.QM) #Statistical Methods in Clinical Trials #msc:62-07 #msc:62P10 #q-bio.MN #q-bio.QM
paper · pdf · doi:10.48550/arxiv.q-bio/0402048
Submitted for publication on 8/14/03
arxiv created 2004/02/29 · openalex publication_date 2004/02/29 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Microarray data are often used to determine which genes are differentially expressed between groups, for example, between treatment and control groups. There are methods of determining which genes have a high probability of differential expression, but those methods depend on the estimation of probability densities. Theoretical results have shown such estimation to be unreliable when high-probability genes are identified. The genes that are probably differentially expressed can be found using decision theory instead of density estimation. Simulations show that the proposed decision-theoretic method is much more reliable than a density-estimation method. The proposed method is used to determine which genes to consider differentially expressed between patients with different types of cancer. The proposed method determines which genes have a high probability of differential expression. It can be applied to data sets that have replicate microarrays in each of two or more groups of patients or experiments.