Ha Sook S, Kim Inyoung, Wang Yue, Xuan Jianhua
Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.
Comp Funct Genomics. 2011;2011:463645. doi: 10.1155/2011/463645. Epub 2011 May 22.
Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
传统上，基于通路的分析假定通路中的基因对生物学功能有同等贡献，因此赋予基因统一权重。然而，这一假设已被证明是错误的，在通路分析中应用统一权重可能并非适用于疾病分子分类等任务的合适方法，因为功能组中的基因可能具有不同的预测能力。因此，我们建议在基于通路的分析中对基因使用不同权重，并设计了四种加权方案。我们将它们应用于两种现有的通路分析方法，使用了真实和模拟的通路基因表达数据。在所有方案中，随机加权方案（即生成随机权重并选择使目标函数最小化的最优权重）在降低P值或错误率方面表现最佳。加权改变了通路评分，并引出了一些新的显著通路，从而能够检测到在统一权重下被遗漏的疾病相关基因。
