CLUSTERING MICROARRAY GENE EXPRESSION DATA USING FUZZY C-MEANS AND DTW DISTANCE

Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. Gene expressions are expected to vary not only in terms of expression amplitudes, but also in terms of time progression since biological processes may unfold with different rates in response to different experimental conditions or within different organisms and individuals. Any distance (Euclidean, Manhattan, ...) which aligns the i'th point on one expression with the i'th point on the other will produce a poor similarity score. In this paper we use DTW distance to attain expression's similarity.