Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods
Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods
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DOI：
10.1109/tcbb.2012.48
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发表时间：
2012-07-01
影响因子：
4.5
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Kawano, Shuichi;Shimamura, Teppei;Miyano, Satoru
我们提出了一种统计方法来揭示表征癌症异质性的基因通路。为了将知识的途径到模型中，我们定义了一组活动的途径，从微阵列基因表达数据的基础上稀疏概率主成分分析（SPPCA）。然后针对癌症表型制定途径活性逻辑回归模型。为了选择与二进制癌症表型相关的途径活动，我们使用弹性网络进行参数估计，并推导出用于选择模型估计中包含的调谐参数的模型选择标准。我们提出的方法还可以基于已识别的多个途径对基因网络进行反向工程，使我们能够发现与癌症表型相关的新基因-基因关联。我们通过对乳腺癌基因表达数据的分析，说明了所提出的方法的整个过程。
We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.