EDISA

EDISA is a software tool that has been developed to address the challenge of identifying gene expression modules from gene-condition-time datasets. Cells are known to dynamically adapt their gene expression patterns in response to various stimuli, and this response is orchestrated into a number of gene expression modules consisting of co-regulated genes. With the growing availability of publicly available microarray datasets, it has become possible to identify such modules by monitoring expression changes over time. However, these time-series datasets can be challenging to analyze, and standard clustering or biclustering methods may not be applicable.

EDISA presents a novel probabilistic clustering approach for 3D gene-condition-time datasets in this context. The tool samples initial modules from the dataset, which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. In a synthetic dataset, the algorithm successfully recovered implanted modules over a range of background noise intensities.

One of the significant contributions of EDISA is the definition of three biologically relevant module types: independent response profiles, coherent modules with similar responses under all conditions, and a response specific to a single condition. These modules are essentially different types of biclusters, and the tool enables a more comprehensive view of the gene expression response.