Statistical implications of pooling RNA samples for microarray experiments

Xuejun Peng, Constance L. Wood, Eric M. Blalock, Kuey Chu Chen, Philip W. Landfield, Arnold J. Stromberg

Research output: Contribution to journalArticlepeer-review

256 Scopus citations

Abstract

Background: Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated. Results: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost. Conclusions: Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.

Original languageEnglish
Article number26
JournalBMC Bioinformatics
Volume4
DOIs
StatePublished - Jun 24 2003

Keywords

  • Microarray experiment design
  • Pooling
  • Power
  • Replicates
  • Sample size estimation

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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