Journal
NUCLEIC ACIDS RESEARCH
Volume 41, Issue 8, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkt145
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Funding
- China Scholarship Council
- National Institutes of Health
- NHLBI [1R01AR057120-01, 1R01HL106034-01]
- National Basic Research Program [2012CB944600]
- Ministry of Science and Technology [2011BAI09B00, 2007AA02Z312]
- Ministry of Health [201002007]
- National Science Foundation of China [30890034]
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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.
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