HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.
Aged
Algorithms
Carcinoma, Pancreatic Ductal
/ genetics
Computer Simulation
Data Analysis
Databases, Genetic
/ statistics & numerical data
Datasets as Topic
Feasibility Studies
Female
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Male
Middle Aged
Models, Genetic
Oligonucleotide Array Sequence Analysis
/ statistics & numerical data
Pancreatic Neoplasms
/ genetics
Prognosis
RNA-Seq
/ statistics & numerical data
Republic of Korea
/ epidemiology
Survival Analysis
Hierarchical structured component model
Pathway analysis
Survival phenotype
Journal
Genes
ISSN: 2073-4425
Titre abrégé: Genes (Basel)
Pays: Switzerland
ID NLM: 101551097
Informations de publication
Date de publication:
14 11 2019
14 11 2019
Historique:
received:
23
09
2019
revised:
06
11
2019
accepted:
07
11
2019
entrez:
20
11
2019
pubmed:
20
11
2019
medline:
6
5
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31739607
pii: genes10110931
doi: 10.3390/genes10110931
pmc: PMC6896173
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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