Empirical characterization of the expression ratio noise structure in high-density oligonucleotide arrays

BackgroundHigh-density oligonucleotide arrays (HDONAs) are a powerful tool for assessing differential mRNA expression levels. To establish the statistical significance of an observed change in expression, one must take into account the noise introduced by the enzymatic and hybridization steps, called type I noise. We undertake an empirical characterization of the experimental repeatability of results by carrying out statistical analysis of a large number of duplicate HDONA experiments.ResultsWe assign scoring functions for expression ratios and associated quality measures. Both the perfect-match (PM) probes and the differentials between PM and single-mismatch (MM) probes are considered as raw intensities. We then calculate the log-ratio of the noise structure using robust estimates of their intensity-dependent variance. The noise structure in the log-ratios follows a local log-normal distribution in both the PM and PM-MM cases. Significance relative to the type I noise can therefore be quantified reliably using the local standard deviation (SD). We discuss the intensity dependence of the SD and show that ratio scores greater than 1.25 are significant in the mid- to high-intensity range.ConclusionsThe noise inherent in HDONAs is characteristically dependent on intensity and can be well described in terms of local normalization of log-ratio distributions. Therefore, robust estimates of the local SD of these distributions provide a simple and powerful way to assess significance (relative to type I noise) in differential gene expression, and will be helpful in practice for improving the reliability of predictions from hybridization experiments.

[1]  C. Li,et al.  Analyzing high‐density oligonucleotide gene expression array data , 2001, Journal of cellular biochemistry.

[2]  C. Li,et al.  Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[4]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

[5]  Cheng Li,et al.  Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application , 2001, Genome Biology.

[6]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[7]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[8]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[9]  D. Slonim,et al.  Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls , 2001, Genome Biology.

[10]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[11]  E. Winzeler,et al.  Genomics, gene expression and DNA arrays , 2000, Nature.

[12]  Felix Naef,et al.  From features to expression: High-density oligonucleotide array analysis revisited , 2001 .

[13]  James L. Winkler,et al.  Accessing Genetic Information with High-Density DNA Arrays , 1996, Science.

[14]  S. P. Fodor,et al.  High density synthetic oligonucleotide arrays , 1999, Nature Genetics.

[15]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  M. Oh,et al.  Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. , 2001, Nucleic acids research.

[17]  E. Chudin,et al.  Assessment of the relationship between signal intensities and transcript concentration for Affymetrix GeneChip® arrays , 2001, Genome Biology.