Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

• Type of Material: Article
• Authors: Suyan Tian, Jilin University; Howard Chang, Emory University; Chi Wang, University of Kentucky
• Language: English
• Date: 2016-09-29
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2016 The Author(s). The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13062-016-0152-3
• Title of Journal or Parent Work: Biology Direct
• ISSN: 1745-6150
• Volume: 11
• Issue: 1
• Start Page: 50
• End Page: 50
• Grant/Funding Information: This study was supported by a fund (No. 31401123) from the Natural Science Foundation of China.
• Keywords: Weighted gene expression profiles; GENOMIC DATA; IMPROVER; LASSO; LINEAR-MODELS; Pathway-based feature selection; CANCER-PATIENTS; REGULARIZATION; Life Sciences & Biomedicine; Multiple sclerosis (MS); PENALIZED REGRESSION; Pathway knowledge; CLASSIFICATION; NETWORK; Non-small cell lung cancer (NSCLC); Science & Technology; Biology; Significance analysis of microarray (SAM)
• Research Categories: Biology, Genetics; Biology, Biostatistics

Abstract

Background: It has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method. Methods: In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs. It ignores the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then combing these weights with the test statistics. Results: Using both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that the weighted version outperforms its original version. Conclusions: To conclude, the additional gene connectivity information does faciliatate feature selection.