Tibshirani Robert, Hastie Trevor
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA.
Biostatistics. 2007 Jan;8(1):2-8. doi: 10.1093/biostatistics/kxl005. Epub 2006 May 15.
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).
我们提出了一种检测基因的方法，这些基因在疾病组中，在部分而非所有样本中呈现出异常高的基因表达。这在癌症研究中可能特别有用，因为能够扩增或关闭基因表达的突变通常仅在少数样本中出现。在实际和模拟示例中，新方法的错误发现率往往低于简单的t统计量阈值法。我们还将我们的方法与Tomlins等人（2005年）最近提出的癌症图谱异常值分析方法进行了比较。
