Takahashi Kei-ichiro, Takigawa Ichigaku, Mamitsuka Hiroshi
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan ; Creative Research Institution, Hokkaido University, Sapporo, Hokkaido, Japan.
PLoS One. 2013 Dec 30;8(12):e82890. doi: 10.1371/journal.pone.0082890. eCollection 2013.
Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under only part of all given experimental conditions. We present a software called SiBIC, which from a given expression dataset, first exhaustively enumerates biclusters, which are then merged into rather independent biclusters, which finally are used to generate gene set networks, in which a gene set assigned to one node has coexpressed genes. We evaluated each step of this procedure: 1) significance of the generated biclusters biologically and statistically, 2) biological quality of merged biclusters, and 3) biological significance of gene set networks. We emphasize that gene set networks, in which nodes are not genes but gene sets, can be more compact than usual gene networks, meaning that gene set networks are more comprehensible. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/miami/faces/index.jsp.
