title: Cancer outlier differential gene expression detection.
abstract: We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic (ORT), which is intuitively motivated from the t-statistic, the most commonly used differential gene expression detection method. Using real and simulation studies, we compare the ORT to the recently proposed cancer outlier profile analysis (Tomlins and others, 2005) and the outlier sum statistic of Tibshirani and Hastie (2006). The proposed method often has more detection power and smaller false discovery rates. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/ort.html.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA. baolin@biostat.umn.edu
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
mesh_terms: D001699:Biometry; D001943:Breast Neoplasms / Q000235:genetics; D004273:DNA, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D009857:Oncogenes
chemical_list: D004273:DNA, Neoplasm