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A0841
Title: Finding latent signals of dynamic correlation in high-throughput expression data Authors:  Tianwei Yu - Emory University (United States) [presenting]
Abstract: In high-throughput biological data, dynamic correlation, i.e. changing correlation patterns under different biological conditions, can reveal important regulatory mechanisms. Current methods seek underlying conditions of dynamic correlation by using certain genes as surrogate signals. We describe a new method that directly identifies strong latent signals that regulate the dynamic correlation of many pairs of genes, named LDCA: Latent Dynamic Correlation Analysis. We validate the performance of the method with extensive simulations. In real data analysis, the method reveals biologically plausible latent factors that were not found by existing methods.