Generalized gene co-expression analysis via subspace clustering using low-rank representation

Tongxin Wang, Jie Zhang, Kun Huang

Research output: Contribution to journalArticle

Abstract

Background: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. Results: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. Conclusions: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms.

Original languageEnglish (US)
Article number196
JournalBMC Bioinformatics
Volume20
DOIs
StatePublished - May 1 2019
Externally publishedYes

Fingerprint

Subspace Clustering
Cluster Analysis
Genes
Gene
Gene Expression
Gene Regulatory Networks
Network Analysis
Electric network analysis
Module
RNA Sequence Analysis
Similarity Measure
Computational Biology
Biomedical Research

Keywords

  • Gene co-expression network analysis
  • Low-rank representation
  • Subspace clustering

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Generalized gene co-expression analysis via subspace clustering using low-rank representation. / Wang, Tongxin; Zhang, Jie; Huang, Kun.

In: BMC Bioinformatics, Vol. 20, 196, 01.05.2019.

Research output: Contribution to journalArticle

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