Primary Research Paper

Reproducibility assessment of independent component analysis of expression ratios from DNA microarrays

  1. David Philip Kreil1,2,*,
  2. David J. C. MacKay2

Article first published online: 23 JUN 2003

DOI: 10.1002/cfg.298

Issue

Comparative and Functional Genomics

Comparative and Functional Genomics

Volume 4, Issue 3, pages 300–317, June 2003

Additional Information

How to Cite

Kreil, D. P. and MacKay, D. J. C. (2003), Reproducibility assessment of independent component analysis of expression ratios from DNA microarrays. Comparative and Functional Genomics, 4: 300–317. doi: 10.1002/cfg.298

Author Information

  1. 1

    Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK

  2. 2

    Inference Group, Cavendish Laboratory, Madingley Road, Cambridge CB3 0HE, UK

*Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK.

Publication History

  1. Issue published online: 23 JUN 2003
  2. Article first published online: 23 JUN 2003
  3. Manuscript Accepted: 4 APR 2003
  4. Manuscript Revised: 30 MAR 2003
  5. Manuscript Received: 6 NOV 2002

Funded by

  • Medical Research Council, UK. Grant Number: G81/555
  • Gatsby Foundation

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Keywords:

  • independent component analysis;
  • expression ratios;
  • microarray data;
  • ensemble learning

Abstract

DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next to a neutral control, examining relative changes (ratios). Independent component analysis (ICA) is a promising modern method for the analysis of such experiments. The condition of ICA algorithms can, however, depend on the characteristics of the data examined, making algorithm properties such as robustness specific to the given application domain. To address the lack of studies examining the robustness of ICA applied to microarray measurements, we report on the stability of variational Bayesian ICA in this domain. Microarray data are usually preprocessed and transformed. Hence we first examined alternative transforms and data selections for the smallest modelling reconstruction errors. Log-ratio data are reconstructed better than non-transformed ratio data by our linear model with a Gaussian error term. To compare ICA results we must allow for ICA invariance under rescaling and permutation of the extracted signatures, which hold the loadings of the original variables (gene transcript ratios) on particular latent variables. We introduced a method to optimally match corresponding signatures between sets of results. The stability of signatures was then examined after (1) repetition of the same analysis run with different random number generator seeds, and (2) repetition of the analysis with partial data sets. The effects of both dropping a proportion of the gene transcript ratios and dropping measurements for several samples have been studied. In summary, signatures with a high relative data power were very likely to be retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wild-type vs. wild-type experiments, moreover, yielded 10 reliably identified signatures, demonstrating that the variance observed is not just noise. Copyright © 2003 John Wiley & Sons, Ltd.

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