A comparison of four clustering methods for brain expression microarray data |
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Authors: | Alexander L Richards Peter Holmans Michael C O'Donovan Michael J Owen Lesley Jones |
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Institution: | 1.Department of Psychological Medicine, School of Medicine,University Hospital Wales,Wales,UK |
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Abstract: | 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. |
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Keywords: | |
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