Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.
Butte and Kohane REF described a technique to find functional genomic clusters in RNA expression data by computing the entropy of gene expression patterns and the mutual information between RNA expression patterns for each pair of genes.
Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements
Pacific Symposium on Biocomputing
Computer Science Medicine