Integrated Analysis of MiRNA-MRNA Expression Profiles

• Activity Number: 193
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Dake Yang*
• Keywords: MicroRNA ; Sparse Canonical correlation analysis ; gene set enrichment analysis ; simulation study ; miRNA-mRNA expression data ; neural tube

Abstract

MicroRNAs (miRNAs) are a large number of small endogenous non-coding RNA molecules (18-25 nucleotides in length) which regulate expression of genes post-transcriptionally. While a variety of algorithms exist for determining the targets of miRNAs, they are generally based on sequence information and frequently produce lists consisting of thousands of genes. Canonical correlation analysis (CCA) is a multivariate statistical method that can be used to find linear relationships between two data sets, and here we apply CCA to find the linear combination of differentially expressed miRNAs and their corresponding target genes having maximal negative correlation. Due to the high dimensionality, sparse CCA is used to constrain the problem and obtain a solution. A novel gene set enrichment analysis statistic is proposed based on the sparse CCA results for estimating the significance of predefined gene sets. The methods are illustrated with both a simulation study and real miRNA-mRNA expression data concerning the murine embryonic developing neural tube.