Background: DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.
Richards et al. REF compared results of four clustering algorithms namely, CRC, k-means, ISA, and memISA on datasets of microarray brain expression.
A comparison of four clustering methods for brain expression microarray data
BMC Bioinformatics
BMC Bioinformatics
Biology Computer Science Medicine