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Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood.

ABSTRACT: In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes.We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which nucleotide has been conjugated to the fluorescent dye. A method is proposed to account for gene-specific dye bias within a maximum-likelihood error modeling framework. Using two different labeling schemes, we show that correcting for gene-specific dye bias results in the superior identification of differentially expressed genes within this framework. Improvement is also possible in related ANOVA approaches.A software implementation of this procedure is freely available at http://cellcircuits.org/VERA

SUBMITTER: Kelley R

PROVIDER: S-EPMC2811084 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood.

Kelley Ryan R Feizi Hoda H Ideker Trey T

Bioinformatics (Oxford, England) 20070710 1

<h4>Motivation</h4>In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes.<h4>Results</h4>We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which n ...[more]

PMID: 17623705

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Project description:BackgroundThe UN's Sustainable Development Goals are devoted to eradicate a range of infectious diseases to achieve global well-being. These efforts require monitoring disease transmission at a level that differentiates between pathogen variants at the genetic/molecular level. In fact, the advantages of genetic (molecular) measures like multiplicity of infection (MOI) over traditional metrics, e.g., R0, are being increasingly recognized. MOI refers to the presence of multiple pathogen variants within an infection due to multiple infective contacts. Maximum-likelihood (ML) methods have been proposed to derive MOI and pathogen-lineage frequencies from molecular data. However, these methods are biased.Methods and findingsBased on a single molecular marker, we derive a bias-corrected ML estimator for MOI and pathogen-lineage frequencies. We further improve these estimators by heuristical adjustments that compensate shortcomings in the derivation of the bias correction, which implicitly assumes that data lies in the interior of the observational space. The finite sample properties of the different variants of the bias-corrected estimators are investigated by a systematic simulation study. In particular, we investigate the performance of the estimator in terms of bias, variance, and robustness against model violations. The corrections successfully remove bias except for extreme parameters that likely yield uninformative data, which cannot sustain accurate parameter estimation. Heuristic adjustments further improve the bias correction, particularly for small sample sizes. The bias corrections also reduce the estimators' variances, which coincide with the Cramér-Rao lower bound. The estimators are reasonably robust against model violations.ConclusionsApplying bias corrections can substantially improve the quality of MOI estimates, particularly in areas of low as well as areas of high transmission-in both cases estimates tend to be biased. The bias-corrected estimators are (almost) unbiased and their variance coincides with the Cramér-Rao lower bound, suggesting that no further improvements are possible unless additional information is provided. Additional information can be obtained by combining data from several molecular markers, or by including information that allows stratifying the data into heterogeneous groups.

Dataset Information

Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood.

Publications

Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood.

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