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

Correlation-based pattern discovery from replicated molecular profiling data enables essential data mining tasks, such as discovering biomolecule association networks and functional modules. Unfortunately, the existing approaches are not tailored to 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 targeted for 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 multivariate parsimonious correlation model for replicated molecular profiling data with blind replication mechanisms, and a constrained (less parsimonious) correlation model explicitly considers the informed replication mechanisms. We derive a generalized formula for correlation-based pattern discovery for both blind and informed replication mechanisms. To promote it´s use among the biomedical research community, we develop a correlation-based pattern discovery software with graphical user interface (GUI) for analyzing replicated molecular profiling data.