title: Canonical correlation analysis for RNA-seq co-expression networks.
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.
authors: State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China.
authors: Shengjun
authors: S
authors: Hong
authors: Xiangning
authors: X
authors: Chen
authors: Li
authors: L
authors: Jin
authors: Momiao
authors: M
authors: Xiong
mesh_terms: D001714:Bipolar Disorder / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D005091:Exons; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism; D018307:Neoplasms, Squamous Cell / Q000235:genetics / Q000378:metabolism; D012559:Schizophrenia / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA