SGAClust: Semi-supervised Graph Attraction Clustering of gene expression data

Gene expression data clustering groups genes with similar patterns into a group, while genes exhibit dissimilar patterns into different groups. Traditional partitional gene expression data clustering partitions the entire set of genes into a finite set of clusters which might not reflect co-expression or coherent patterns across all genes belonging to a cluster. In this paper, we propose a graph-theoretic clustering algorithm called GAClust which groups co-expressed genes into the same cluster while also detecting noise genes. Clustering of genes is based on the presumption that co-expressed genes are more likely to share common biological functions. However, it has been observed that the clusters produced by traditional methods often do not reflect true biological groups or functions. To address this issue, we propose a semi-supervised algorithm, SGAClust to produce more biologically relevant clusters. We consider both synthetic and cancer gene expression datasets to evaluate the performance of the proposed algorithms. It has been found that SGAClust outperforms the unsupervised algorithms. Additionally, we also identify potential gene biomarkers which will further help in cancer management.