Fuzzy J-Means and VNS methods for clustering genes from microarray data

Belacel, Nabil; Čuperlović-Culf, Miroslava; Laflamme, Mark; Ouellette, Rodney J.

doi:10.1093/bioinformatics/bth142

Cited by 61 publications

(34 citation statements)

References 21 publications

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“…The exact F-KM formalism is described in detail elsewhere( [15,16] and references therein). Briefly, the membership values and the centroid positions are calculated from the objective function J m (W, V)definedas…”

Section: Fuzzy K-means Clustering Methodologymentioning

confidence: 99%

“…For the NMR spectral profiles of cell lines clustered using F-KM into five sample clusters: R i = 0.80 for absolute value distances; R i = 0.74 for Euclidian distance matrix and R i = 0.75 for cosine dissimilarity matrix. The degree of fuzziness in the clustering process is regulated by the fuzziness parameter, m,w i t hm = 1 giving the crisp clustering and with an increasing fuzziness of the result with m increasing ultimately leading to clustering result for all points being w ik = 1/c for all i and k. A previously devised empirical rule about the optimal m parameter [15,24] suggests that an optimal m value should lead to (i) the median of the top membership values being ≥0.5 (prevents the results from being overly fuzzy) and (ii) the median of all membership values being ≥0 (prevents the results from becoming crisp). The analysis has shown that for these datasets an optimal value of m is 2, which is in agreement with the value originally suggested by Bezadek [16] for the general application of F-KM.…”

Section: Fuzzy K-means Clustering Methodologymentioning

confidence: 99%

“…The result of fuzzy clustering calculation is the matrix of membership degrees that describe the level of similarity between each feature and each cluster centroid. Several different fuzzy clustering methods utilising different approaches for the calculation of centroids as well as membership values were developed including fuzzy K-means (F-KM), [16,17] fuzzy J-means [15] and fuzzy SOM. [18] These and other fuzzy methods were previously utilised in the analysis of transcriptomics data with excellent results.…”

Section: S97mentioning

confidence: 99%

“…[18] These and other fuzzy methods were previously utilised in the analysis of transcriptomics data with excellent results. [15,17] The advantage of fuzzy classification becomes particularly apparent in the analysis of overlapping groups of objects as well as subgroups in conjunction with the separation of major groups.…”

Section: S97mentioning

confidence: 99%

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NMR metabolic analysis of samples using fuzzy K‐means clustering

Čuperlović-Culf

Belacel

Culf

et al. 2009

Magnetic Reson in Chemistry

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The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the application of fuzzy K-means clustering method for the classification of samples based on metabolomics 1D 1 H NMR fingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animal models. The cell line dataset included NMR spectra of lipophilic cell extracts for two normal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clustering method allowed more accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clustering method.

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Section: Fuzzy K-means Clustering Methodologymentioning

confidence: 99%

Section: Fuzzy K-means Clustering Methodologymentioning

confidence: 99%

Section: S97mentioning

confidence: 99%

Section: S97mentioning

confidence: 99%

See 2 more Smart Citations

NMR metabolic analysis of samples using fuzzy K‐means clustering

Čuperlović-Culf

Belacel

Culf

et al. 2009

Magnetic Reson in Chemistry

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“…This way of partition is more realistic in labeling the regions of foreground spots from the background as well as from possible artifacts. The fuzzy c-means (FCM) based approaches have been introduced for several microarray data analysis [26][27][28][29][30]. In [26,30] FCM was used for grouping biologically relevant genes.…”

Section: Clustering Based Approachesmentioning

confidence: 99%

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Wang

Gunampally

Chen³

et al. 2007

J Sign Process Syst Sign Image

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Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.

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