Unsupervised discovery of fuzzy patterns in gene expression data

Discovering patterns from gene expression levels is regarded as a classification problem when tissue classes of the samples are given and solved as a discrete-data problem by discretizing the expression levels of each gene into intervals maximizing the interdependence between that gene and the class labels. However, when class information is unavailable, discovering gene expression patterns becomes difficult. This paper attempts to tackle this important problem. For a gene pool with large number of genes, we first cluster the genes into smaller groups. In each group, we use the representative gene, one with highest interdependence with others in the group, to drive the discretization of the gene expression levels of other genes. Treating intervals as discrete events, association patterns can be discovered. If the gene groups obtained are crisp clusters, significant patterns overlapping different clusters cannot be found. This paper presents a new method of "fuzzifying" the crisp attribute clusters for that purpose. To evaluate the effectiveness of our approach, we first apply the above described procedure on a synthetic dataset and then a gene expression dataset with known class labels. The class labels are not being used in both analyses but used later as the ground truth in a classificatory problem for assessing the algorithm's effectiveness in fuzzy gene clustering and discretization. The results show the efficacy of the proposed method.