cMonkey identifies relevant conditions in which the genes within a given bicluster (where “biclustering” is condition- or cell-state-specific clustering) are expected to be co-regulated (importantly, in later stages of analysis we use only these conditions to learn TFs and EFs that influence each bicluster). The methods separates the calculation of the score components associated with each datatype into individual calculations but still effectively sample biclusters that optimally satisfy multiple model components (each representing a separate data-type). The method was designed as a preprocessing step for network inference and performed well in comparison to all other methods tested when the trade-off between sensitivity, specificity, and coverage (fraction of conditions and genes included in one or more biclusters) were considered, particularly in context of the other bulk characteristics (cluster size, residual, etc.).
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cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism.
Reiss DJ, Plaisier CL, Wu WJ, Baliga NS.
Nucleic Acids Res. 2015 Apr 14. pii: gkv300.
BMC Bioinformatics. 2006 Jun 2;7:280.
Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks.
Reiss DJ, Baliga NS, Bonneau R.