Group spike-And-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information

Zaixiang Tang, Yueping Shen, Yan Li, Xinyan Zhang, Jia Wen, Chen'Ao Qian, Wenzhuo Zhuang, Xinghua Shi, Nengjun Yi

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Motivation Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. Results We propose new Bayesian hierarchical generalized linear models, called group spike-And-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-Adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. Availability and implementation The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Contact [email protected] Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
Pages (from-to)901-910
Number of pages10
JournalBioinformatics
Volume34
Issue number6
DOIs
StatePublished - Mar 15 2018

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