Gene set analysis with graph-embedded kernel association test
Year: 2021
Type: article
Abstract: MOTIVATION: Kernel-based association test (KAT) has been a popular approach to evaluate the association of expressions of a gene set (e.g. pathway) with a phenotypic trait. KATs rely on kernel functions which capture the sample similarity across multiple features, to capture potential linear or non-linear relationship among features in a gene set. When calculating the kernel functions, no network graphical information about the features is considered. While genes in a functional group (e.g. a pathway) are not independent in general due to regulatory interactions, incorporating regulatory network (or graph) information can potentially increase the power of KAT. In this work, we propose a graph-embedded kernel association test, termed gKAT. gKAT incorporates prior pathway knowledge when constructing a kernel function into hypothesis testing. RESULTS: We apply a diffusion kernel to capture any graph structures in a gene set, then incorporate such information to build a kernel function for further association test. We illustrate the geometric meaning of the approach. Through extensive simulation studies, we show that the proposed gKAT algorithm can improve testing power compared to the one without considering graph structures. Application to a real dataset further demonstrate the utility of the method. AVAILABILITY AND IMPLEMENTATION: The R code used for the analysis can be accessed at https://github.com/JialinQu/gKAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Jialin Qu, Yuehua Cui
Institution: Michigan State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4200570290