Biostatistics 4:249-264 (2003)
© 2003 Oxford University Press
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Department of Biostatistics, Johns Hopkins University, Baltimore MD 21205, USA rafa{at}jhu.edu
Division of Genetics and Bioinformatics, WEHI, Melbourne, Australia
Gene Logic Inc., Berkeley, CA, USA
Gene Logic Inc., Gaithersburg, MD, USA
Gene Logic Inc., Gaithersburg, MD, USA
Gene Logic Inc., Gaithersburg, MD, USA
Division of Genetics and Bioinformatics, WEHI, Melbourne, Australia. Department of Statistics, University of California at Berkeley
*To whom correspondence should be addressed
In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip® system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip® arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip® arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip® arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance–mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
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