Chapter 8. Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Michael J Boedigheimer1,
  2. Jeff W Chou2,
  3. J Christopher Corton3,
  4. Jennifer Fostel2,
  5. Raegan O'Lone4,
  6. P Scott Pine5,
  7. John Quackenbush6,
  8. Karol L Thompson5 and
  9. Russell D Wolfinger7

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch8

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Boedigheimer, M. J., Chou, J. W., Corton, J. C., Fostel, J., O'Lone, R., Pine, P. S., Quackenbush, J., Thompson, K. L. and Wolfinger, R. D. (2009) Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch8

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    Amgen Inc., Thousand Oaks, CA, USA

  2. 2

    National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA

  3. 3

    US Environmental Protection Agency, Research Triangle Park, NC, USA

  4. 4

    ILSI Health and Environmental Sciences Institute, Washington, DC, USA

  5. 5

    Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

  6. 6

    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

  7. 7

    SAS Institute Inc., Cary, NC, USA

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:

  • toxicogenomics;
  • smooth bias;
  • baseline expression;
  • loess;
  • variance;
  • microarray;
  • fasting;
  • strain

Summary

To characterize variability in baseline gene expression, the ILSI Health and Environmental Sciences Institute Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment recently compiled a large data set from 536 Affymetrix arrays for rat liver and kidney samples from control groups in toxicogenomics studies. Institution was one of the prominent sources of variability, which could be due to differences in how studies were performed or to systematic biases in the signal data. To assess the contribution of smooth bias to variance in the baseline expression data set, the robust multi-array average data were further processed by applying loess normalization and the degree of smooth bias within a data set was characterized. Bias correction did not have a large effect on the results of analyses of the major sources of variance but did affect the identification of genes associated with certain study factors if significant smooth bias was present within the data set.