Validating clustering for gene expression data
Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level.Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. “Validating Clusterings of Gene Expression Data.” In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 1–245. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering. ER - We propose a measure for the validation of clusterings of gene expression data.We call the new methodology , and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters.The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions.