FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

By Limin Fu, Enzo Medico. BMC Bioinformatics, 2007

548 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/aae998c7d4799422f7e34f70ac253d5b524d0fb4

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Citations in the corpus

dbscan: Fast Density-Based Clustering with R (2019, background): “0 CHAMELEON algorithm (Karypis, Han, and Kumar 1999), along with a variety of shape data sets commonly found in clustering validation papers (Gionis, Mannila, and Tsaparas 2007; Zahn 1971; Chang and Yeung 2008; Jain and Martin 2005; Fu and Medico 2007).”
Identification of cell types from single-cell transcriptomes using a novel clustering method (2015, unknown): “The dataset shown in Figure 1F consists of two clusters with hardly defined border and shape, which represents a difficult case of clustering (Fu and Medico, 2007).”
A molecularly annotated platform of patient-derived xenografts ("xenopatients") identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. (2011, methodology): “The results were plotted as a heat-map using the gedas software (49).”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, background): “The third data set, Flame [30] , is of different size and shape.”