Shahbaba Babak, Tibshirani Robert, Shachaf Catherine M, Plevritis Sylvia K
Department of Statistics, University of California, Irvine, CA, USA.
J R Stat Soc Ser C Appl Stat. 2011 Aug 1;60(4):541-557. doi: 10.1111/j.1467-9876.2011.00765.x.
We propose a hierarchical Bayesian model for analyzing gene expression data to identify pathways differentiating between two biological states (e.g., cancer vs. non-cancer and mutant vs. normal). Finding significant pathways can improve our understanding of biological processes. When the biological process of interest is related to a specific disease, eliciting a better understanding of the underlying pathways can lead to designing a more effective treatment. We apply our method to data obtained by interrogating the mutational status of p53 in 50 cancer cell lines (33 mutated and 17 normal). We identify several significant pathways with strong biological connections. We show that our approach provides a natural framework for incorporating prior biological information, and it has the best overall performance in terms of correctly identifying significant pathways compared to several alternative methods.
我们提出了一种用于分析基因表达数据的分层贝叶斯模型，以识别区分两种生物学状态（例如，癌症与非癌症、突变与正常）的通路。找到显著的通路可以增进我们对生物学过程的理解。当感兴趣的生物学过程与特定疾病相关时，更好地理解潜在通路有助于设计更有效的治疗方法。我们将我们的方法应用于通过询问50个癌细胞系（33个突变的和17个正常的）中p53的突变状态而获得的数据。我们识别出了几条具有强烈生物学联系的显著通路。我们表明，我们的方法提供了一个纳入先验生物学信息的自然框架，并且与几种替代方法相比，在正确识别显著通路方面具有最佳的整体性能。
