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Assisted clustering of gene expression data using ANCut.

Abstract：

BACKGROUND:In biomedical research, gene expression profiling studies have been extensively conducted. The analysis of gene expression data has led to a deeper understanding of human genetics as well as practically useful models. Clustering analysis has been a critical component of gene expression data analysis and can reveal the (previously unknown) interconnections among genes. With the high dimensionality of gene expression data, many of the existing clustering methods and results are not as satisfactory. Intuitively, this is caused by "a lack of information". In recent profiling studies, a prominent trend is to collect data on gene expressions as well as their regulators (copy number alteration, microRNA, methylation, etc.) on the same subjects, making it possible to borrow information from other types of omics measurements in gene expression analysis. METHODS:In this study, an ANCut approach is developed, which is built on the regularized estimation and NCut techniques. An effective R code that implements this approach is developed. RESULTS:Simulation shows that the proposed approach outperforms direct competitors. The analysis of TCGA (The Cancer Genome Atlas) data further demonstrates its satisfactory performance. CONCLUSIONS:We propose a more effective clustering analysis of gene expression data, with the assistance of information from regulators. It provides a new venue for analyzing gene expression data based on the assisted analysis strategy.

journal_name

BMC Genomics

journal_title

BMC genomics

authors

Teran Hidalgo SJ,Wu M,Ma S

doi

10.1186/s12864-017-3990-1

subject

Has Abstract

pub_date

2017-08-16 00:00:00

pages

623

issue

1

issn

1471-2164

pii

10.1186/s12864-017-3990-1

journal_volume

18

pub_type

杂志文章

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