title: A revised design for microarray experiments to account for experimental noise and uncertainty of probe response.
abstract: BACKGROUND
  Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance.
  
  
  RESULTS
  Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements.
  
  
  CONCLUSIONS
  The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.
authors.affiliation: Max-Planck-Institut für Evolutionsbiologie, Plön, Germany; Department of Periodontics, School of Dentistry, University of Washington, Seattle, Washington, United States of America.
authors.forename: Alex E
authors.initials: AE
authors.lastname: Pozhitkov
authors.affiliation: Department of Periodontics, School of Dentistry, University of Washington, Seattle, Washington, United States of America; Ph.D Microbiology Program, Department of Biological Sciences, Alabama State University, Montgomery, Alabama, United States of America.
authors.forename: Peter A
authors.initials: PA
authors.lastname: Noble
authors.affiliation: Max-Planck-Institut für Evolutionsbiologie, Plön, Germany; National Centre for Biotechnology Education, University of Reading, Reading, United Kingdom.
authors.forename: Jarosław
authors.initials: J
authors.lastname: Bryk
authors.affiliation: Max-Planck-Institut für Evolutionsbiologie, Plön, Germany.
authors.forename: Diethard
authors.initials: D
authors.lastname: Tautz
mesh_terms: D000818:Animals; D002138:Calibration; D056915:DNA Copy Number Variations; D020869:Gene Expression Profiling; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards*; D015203:Reproducibility of Results