CLAG: an unsupervised non hierarchical clustering algorithm handling biological data

• Published in: BMC bioinformatics Vol. 13; no. 1; p. 194
• Main Authors: Dib, Linda, Carbone, Alessandra
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.08.2012; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Bioinformatics; Biological research; Biology, Experimental; Brain Neoplasms - genetics; Breast Neoplasms - genetics; Cancer; Cluster Analysis; Convergence; Data processing; Female; Gene Expression; Humans; Information management; Life Sciences; Methods; MicroRNAs - genetics; miRNA; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis; Propagation; protein families; Quantitative Methods; Studies
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-13-194

Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either. CLAG (for CLusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets. CLAG can be used to investigate the cluster structure present in biological datasets and to identify its underlying graph. It showed to be more informative and accurate than several known clustering methods, as hierarchical agglomerative clustering, k-means, fuzzy c-means, model-based clustering, affinity propagation clustering, and not to suffer of the convergence problem proper to this latter.