Evaluation and optimization of clustering in gene expression data analysis A. Fazel Famili, Ganming Liu and Ziying Liu National Research Council of Canada Bioinformatics 2004

Introduction Gene expression data - clustering of genes Identifying potential clusters that contain biologically relevant patterns of gene expression Measure of cluster quality

Existing methods Silhouette value –Silhouette of a cluster is measured on its compactness and distance from closest cluster –Cannot identify proper clusters containing informative genes (illustrated in this study) Neighbor divergence per gene –Needs external information –Uses scientific literature to evaluate whether a groups of genes are functionally related

Existing methods Parametric bootstrap resampling –Requires generation of new observations through resampling –Time consuming Cluster stability score –Cluster stability is obtained by clustering on a random subspace of the attribute space –Cannot work if two subsets randomly sampled contain independent information

Proposed method: Stability Based on cluster’s immovability on partition Immovability: rate at which the contents of a cluster remain unchanged during a clustering process for K = i to i+n (K – number of clusters) Advantages –Accounts for all factors that affect the clustering process –Uses complete dataset

Datasets Four datasets from previous studies in the literature Three simulated datasets

Results for Yeast dataset

Summary of results

Simulated data results

Conclusions Identifying meaningful clusters is important for further processing of data and to ultimately obtain meaningful results Stability –Helps in identifying meaningful clusters –Also helps in finding optimal number of clusters –Improved performance compared to other methods –Does not require external information or subsampling