Fuzzy soft subspace clustering method for gene co-expression network analysis

Clustering techniques for building gene co-expression networks suffer greatly from biological complexities. This paper proposes a fuzzy soft subspace clustering method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific feature subspaces of clusters and interrelations among different clusters can be extracted by this method, providing useful clues for gene co-expression network analysis. Experiments on yeast cell cycle data have shown that this method is effective in extracting biological relationships between functional gene clusters, and enhancing gene co-expression network analysis.