RNA-Seq: Canonical correlation analysis for RNA-seq co-expression networks.

Newsbot! · 03-06-2013, 03:00 AM

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Canonical correlation analysis for RNA-seq co-expression networks.

Nucleic Acids Res. 2013 Mar 4;

Authors: Hong S, Chen X, Jin L, Xiong M

Abstract
Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels.

PMID: 23460206 [PubMed - as supplied by publisher]

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03-06-2013, 03:00 AM	#1
Newsbot! RSS Posting Maniac Join Date: Feb 2008 Posts: 1,443	RNA-Seq: Canonical correlation analysis for RNA-seq co-expression networks. Syndicated from PubMed RSS Feeds Canonical correlation analysis for RNA-seq co-expression networks. Nucleic Acids Res. 2013 Mar 4; Authors: Hong S, Chen X, Jin L, Xiong M Abstract Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels. PMID: 23460206 [PubMed - as supplied by publisher] More...