Chapter 15. Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Lev Klebanov1 and
  2. Andreas Scherer Founder/CEO of Spheromics2

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch15

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Klebanov, L. and Scherer, A. (2009) Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch15

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    Department of Probability and Statistics, Charles University, Prague, Czech Republic

  2. 2

    Spheromics, Kontiolahti, Finland

Publication History

  1. Published Online: 2 NOV 2009
  2. Published Print: 30 OCT 2009

ISBN Information

Print ISBN: 9780470741382

Online ISBN: 9780470685983

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Keywords:

  • microarray data;
  • biological noise;
  • aggregation effect;
  • gene expression levels;
  • covariance;
  • random number of cells;
  • Simpson's paradox

Summary

Aggregation effect in microarray data analysis distorts the correlations between gene expression levels and, in some sense, plays a role of technical noise. This aspect is especially important in network and association inference analyses. However, it is possible to construct statistical estimators which take aggregation into account to generate ‘clean’ covariance of expression levels. Based on this estimator, we provide a method to find gene pairs having essentially different correlation structure.