Abstract Clustering analysis of gene expression data based on similar expression measures can not fully reveal the genetic function similarity between genes. Combined with gene transitive co-expression, a method for clustering analysis based on transitive co-expression is proposed to solve the problem. Firstly, the gene-related graph is built by using coefficient between gene expression profiles. Next, the transitive co-expression relationship between genes is obtained by the shortest path analysis. Then, clustering is performed by using k-means algorithm with transitive co-expression relationship as similarity measure. The experiments on Yeast gene expression data show that the transitive co-expression-based clustering method achieves better clustering performance compared with expression-based clustering method, and the clustering accuracy is significantly higher than that of the expression-based clustering method. The experimental results indicate that the proposed algorithm has better performance in revealing the nature of gene similarity compared with expression-based clustering method.
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