• Triclustering;
  • Biclustering;
  • Simultaneous clustering;
  • Three-way clustering;
  • Multi-source;
  • Multi-way analysis;
  • Biological data analysis.


We present a software tool, called TriClust, for multi-way analysis of gene expression data from paired conditions of multiple organisms. The analysis is based on a new concept called triclustering, which is an extension of biclustering over a third dimension that represents the organism where the microarray experiment is performed. TriClust provides a comprehensive analysis of co-regulated genes under a subset of experimental conditions over multiple organisms. The results are visualized using heat-maps and the Gene Ontology (GO) term enrichment statistics. The experimental results indicate that TriClust can successfully identify biologically significant triclusters and promote a useful tool for cross species analysis of gene regulation from microarray expression data. The statistical results suggest that, when available, triclustering on multi-organism data can result in better gene clusters in comparison to biclustering on single-organism data. The TriClust software is publicly available as a standalone program.