A Generalized Multivariate Approach for Correlation-Based Pattern Discovery from Replicated Molecular Profiling Data

Abstract

Correlation-based pattern discovery from replicatedmolecular profiling data enables essential data mining tasks, such as discovering biomolecule association networks and functional modules. Unfortunately, the existing approaches are not tailoredto analyze replicated measurements, which is further confused by various replication mechanisms. With few exception, existing approaches average or summarize over replicates of diverse magnitude, which might wipe out important patterns of low magnitude and/or cancel out patterns of similar magnitude. The averaging or summarizing procedure, originally targetedfor univariate differential expression analysis, has become a nuisance in multivariate correlation-based pattern discovery. Multivariate approaches that treat each replicate individually provide a promising alternative. Here we propose a multivariateparsimonious correlation model for replicated molecular profilingdata with blind replication mechanisms, and a constrained (lessparsimonious) correlation model explicitly considers the informedreplication mechanisms. We derive a generalized formula forcorrelation-based pattern discovery for both blind and informedreplication mechanisms. To promote it’s use among the biomedicalresearch community, we develop a correlation-based patterndiscovery software with Graphical User Interface (GUI) foranalyzing replicated molecular profiling data.