Mimosa: Mixture Model of Co-expression to Detect Modulators of Regulatory Interaction

Background: Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Results: Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown co...

• Type: Conference
• Year: 2009
• Where: WABI
• Authors: Matthew Hansen, Logan Everett, Larry Singh, Sridhar Hannenhalli
• Keywords: Algorithms, Expression Patterns, Expression Samples, Regulatory Interactions, WABI 2009