Bootstrapping cluster analysis: assessing the reliability of conclusions

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• Jules Griffin ( Department of Biochemistry, University of Cambridge)
• Friday 27 July 2007, 14:00-15:00
• Meeting room 1 cambridge system biology centre.

If you have a question about this talk, please contact Dr N Karp.

Presenting the paper by Kerr MK & Churchill GA PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 98 (16): 8961-8965 JUL 31 2001

(paper available through ISI Web of knowledge)

Abstract:We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

This talk is part of the Bioinformatics jounal club for the -omics series.

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