HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data
by
Lydia Mok 1, Yongkang Kim 2, Sungyoung Lee 3, Sungkyoung Choi 4, Seungyeoun Lee 5, Jin-Young Jang 6 and Taesung Park 1,2,*
Lydia Mok 1, Yongkang Kim 2, Sungyoung Lee 3, Sungkyoung Choi 4, Seungyeoun Lee 5, Jin-Young Jang 6 and Taesung Park 1,2,*
1
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
2
Department of Statistics, Seoul National University, Seoul 08826, Korea
3
Center for Precision Medicine, Seoul National University Hospital, Seoul 03080, Korea
4
Department of Applied Mathematics, Hanyang University (ERICA), Ansan 15588, Korea
5
Department of Mathematics and Statistics, Sejong University, Seoul 05006, Korea
6
Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea
*
Author to whom correspondence should be addressed.
Genes 2019, 10(11), 931; https://doi.org/10.3390/genes10110931
Received: 23 September 2019 / Revised: 6 November 2019 / Accepted: 7 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Selected Papers from the 15th International Conference on Bioinformatics (BIOINFO 2019))
Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.
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MDPI and ACS Style
Mok, L.; Kim, Y.; Lee, S.; Choi, S.; Lee, S.; Jang, J.-Y.; Park, T. HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data. Genes 2019, 10, 931.
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