Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information

BMC Bioinformatics
Zaixiang TangNengjun Yi

Abstract

Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes. We here propose a Bayesian hierarchical Cox survival model, called the group spike-and-slab lasso Cox (gsslasso Cox), for predicting disease survival outcomes and detecting associated genes by incorporating group structures of biological pathways. Our hierarchical model employs a novel prior on the coefficients of genes, i.e., the group spike-and-slab double-exponential distribution, to integrate group structures and to adaptively shrink the effects of genes. We have developed a fast and stable deterministic algorithm to fit the proposed models. We performed extensive simulation studies to assess the model fitting properties and the prognostic performance of the proposed method, and also applied our method to analyze three cancer data sets. Both the theoretical and empirical studies show that t...Continue Reading

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Citations

Aug 23, 2019·Journal of Computational Chemistry·Yuqi ZhangMichel F Sanner
Feb 16, 2021·Frontiers in Cell and Developmental Biology·Binhua TangYongfeng Tao
Oct 15, 2021·PloS One·Naufal Shamilevich ZagidullinValentin Nikolaevich Pavlov

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Software Mentioned

R package BhGLM
BhGLM
grpregOverlap
bioconductor
R
cMCP
clusterProfiler
grlasso
gsslasso
R package SGL

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