Sluttychat sites - Validating clustering for gene expression data bioinformatics

In contrast, common limitations of new methods proposed by bioinformaticians include the requirement of using particular programming environments and the specification of a number of different parameters (e.g., []), which makes their implementation difficult for non-expert users.Motivated by these problems, we present the first large-scale analysis of different clustering methods and proximity measures for clustering cancer tissues (samples).

These data sets were obtained using two microarrays technologies: single-channel Affymetrix chips (21 sets) and double-channel c DNA (14 sets).

We compare seven different types of clustering algorithms: single linkage (SL), complete linkage (CL), average linkage (AL), -means (KM), mixture of multivariate Gaussians (FMG), spectral clustering (SPC) and shared nearest neighbor-based clustering (SNN).

Furthermore, our data sets are not restricted to have only two classes.

Besides the large-scale comparison of clustering methods and proximity measures for cancer gene expression data, a major contribution of this paper is to provide a common group of data sets (benchmark data sets) to be shared among researchers as a stable basis for the evaluation and comparison of different machine learning methods for clustering or classification of cancer gene expression data – available in the supplementary material [).

These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria.

Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated.When applicable, we use four proximity measures together with these methods: Pearson's Correlation coefficient (P), Cosine (C), Spearman's correlation coefficient (SP) and Euclidean Distance (E).Regarding Euclidean distance, we employ the data in four different versions: original ( The data sets present different values for features such as type of microarray chip (second column), tissue type (third column), number of samples (fourth column), number of classes (fifth column), distribution of samples within the classes (sixth column), dimensionality (seventh column) and dimensionality after feature selection (last column).Math [email protected]@[email protected]@ =feaagaart1ev2aaat Cv AUf Ktt Learu Wr P9MDH5MBPb Iq V92Aae Xat Lx BI9g Baebbnrfif Hh DYfgasaac PC6x Ni=x H8vi VGI8Gi=h Eeeu0x Xdbba9fr Fj0xb9qqp G0d Xdb9aspe I8k8fi I fs Y=rq Gq Vepae9pg0db9vqai Vg Fr0xfr=xfr=xc9adbaqaae Gaci Gaaiaabeqaaeqabi Waaa Gcba Waa Oaaaeaacq WGUb GBa [email protected]@ samples.The recovery of the cluster structure is measured using the corrected Rand (c R) index by comparing the actual classes of the tissues samples (e.g., cancer types/subtypes) with the cluster assignments of the tissue samples.Before putting our analysis into perspective with respect to some of the related works, in order to prevent misleading interpretations, it is important to draw the attention to the fact that the problem of clustering cancer gene expression data (tissues) is very different from that of clustering genes.

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