Abstract
BackgroundData clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process.ResultsThe clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License.ConclusionThe FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis.
Highlights
Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays
The first is the extraction of local structure information and identification of cluster supporting objects (CSO's)
Each object is assigned with equal membership to all clusters, with the exception of Cluster Supporting Objects (CSOs) and outlier objects, each CSO being assigned with full membership to itself as a cluster, and all outlier objects being assigned with a full membership to the outlier group
Summary
Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. Since the work of Eisen and colleagues [1], clustering methods have become a key step in microarray data analysis, to identify groups of genes or samples displaying a similar expression profile. None of the existing clustering algorithms performs significantly better than the others when tested across multiple datasets [4,5,6] Used algorithms, such as k-means, hierarchical clustering and Self-Organizing Maps (SOM) [7], typically construct clusters on the basis of pairwise distance between genes. As a consequence, they may fail to reveal nonlinear relationships between gene expression profiles, and thereby fail to correctly represent a dataset with nonlinear structure [8]. Hierarchical clustering remains the most widely used clustering algorithm, it has been described to suffer from a number of limitations mostly deriving from the local decision making scheme that joins the two closest genes or clusters without considering the data as a whole [12]
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