Nonparametric Pathway-Based Regression Models for Analysis of Genomic Data
High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression(NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes within pathways that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevent pathways and genes within pathways. Application to a gene expression data set on breast cancer distant matastasis identified that Wnt, apoptosis and cell cycle regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.
Zhi Wei and Hongzhe Li. "Nonparametric Pathway-Based Regression Models for Analysis of Genomic Data" 2006
Available at: http://works.bepress.com/hongzhe_li/9