Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays

Kevin R. Coombes, W. Edward Highsmith, Tammy A. Krogmann, Keith A. Baggerly, David N. Stivers, Lynne V. Abruzzo

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Microarray experiments involve many steps, including spotting cDNA, extracting RNA, labeling targets, hybridizing, scanning, and analyzing images. Each step introduces variability, confounding our ability to obtain accurate estimates of the biological differences between samples. We ran repeated experiments using high-density cDNA microarray membranes (Research Genetics Human GeneFilters® Microarrays Version I) and 33P-labeled targets. Total RNA was extracted from a Burkitt lymphoma cell line (GA-10). We estimated the components of variation coming from: (1) image analysis, (2) exposure time to PhosphorImager® screens, (3) differences in membranes, (4) reuse of membranes, and (5) differences in targets prepared from two independent RNA extractions. Variation was assessed qualitatively using a clustering algorithm and quantitatively using a version of ANOVA adapted to multivariate microarray data. The largest contribution to variation came from reusing membranes, which contributed 38% of the total variation. Differences in membranes and in exposure time each contributed about 10%. Differences in target preparations contributed less than 5%. The effect of image quantification was negligible. Much of the effect from reusing membranes was attributable to increasing levels of background radiation and can be reduced by using membranes at most four times. The effects of exposure time, which were partly attributable to variation in the scanning process, can be minimized by using the same exposure time for all experiments.

Original languageEnglish (US)
Pages (from-to)655-669
Number of pages15
JournalJournal of Computational Biology
Issue number4
StatePublished - 2002


  • Akaike Information Criterion
  • Analysis of variance
  • Differential gene expression
  • Replicated microarray experiments
  • cDNA microarray

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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