Unsupervised Stability-Based Ensembles to Discover Reliable Structures in Complex Bio-molecular Data
Computational Intelligence Methods for Bioinformatics and Biostatistics
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Motivation: Hierarchical clustering is a common approach to study protein and gene expression data. This unsupervised technique is used to find clusters of genes or proteins which are expressed in a coordinated manner across a set of conditions. Because of both the biological and technical variability, experimental repetitions are generally performed. In this work, we propose an approach to evaluate the stability of clusters derived from hierarchical clustering by taking repeated measurements into account. Results: The method is based on the bootstrap technique that is used to obtain pseudo-hierarchies of genes from resampled datasets. Based on a fast dynamic programming algorithm, we compare the original hierarchy to the pseudo-hierarchies and assess the stability of the original gene clusters. Then a shuffling procedure can be used to assess the significance of the cluster stabilities. Our approach is illustrated on simulated data and on two microarray datasets. Compared to the standard hierarchical clustering methodology, it allows to point out the dubious and stable clusters, and thus avoids misleading interpretations. Availability: The programs were developed in C and R languages. Contact: brehelin@lirmm.fr Supplementary information: Supplementary Material and source code are available at address http://www.lirmm.fr/~brehelin/Stability/