Abstract
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.
Highlights
A biologically significant interest would be to detect genes with similar behavior under certain experimental conditions
The idea behind SiBIC is to enumerate all possible gene behaviors as biclusters, which are summarized into gene set networks, in which each node has a gene set with coexpressed genes under particular experimental conditions
We have presented our software, SiBIC, which generates gene set networks by summarizing biclusters, which are first exhaustively enumerated based on maximal frequent itemset mining
Summary
A biologically significant interest would be to detect genes with similar behavior under certain experimental conditions. SiBIC is a web server, which, given an expression dataset, provides such gene behavior information in a compact manner. The idea behind SiBIC is to enumerate all possible gene behaviors as biclusters, which are summarized into gene set networks, in which each node has a gene set with coexpressed genes under particular experimental conditions. The procedure of SiBIC is as follows: SiBIC first enumerates all biclusters in a given expression dataset which are merged together into a relatively smaller number of rather unique biclusters, from which gene set networks are generated. Gene set networks have a set of genes for nodes, by which each node can have more than one genes. Gene set networks are clearly more advantageous than usual gene networks, because the network size can be kept smaller while genes at each node are coexpressed
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