Outlier sums for differential gene expression analysis.
We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).
Tibshirani
Robert
R
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA. tibs@stat.stanford.edu
Hastie
Trevor
T
D003198:Computer Simulation; D004273:DNA, Neoplasm / Q000235:genetics; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011471:Prostatic Neoplasms / Q000235:genetics
D004273:DNA, Neoplasm