Model-based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application

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Model-based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application

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Title: Model-based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application
Author: Hung Wong, Wing; Li, Cheng

Note: Order does not necessarily reflect citation order of authors.

Citation: Li, Cheng, and Wing Hung Wong. 2001. Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biology 2(8): research0032.1-research0032.11.
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Abstract: Background: A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of accuracy. Here we investigate the stability of the probe-sensitivity index across different tissue types, the reproducibility of results in replicate experiments, and the use of MBEI in perfect match (PM)-only arrays. Results: Probe-sensitivity indexes are stable across tissue types. The target gene's presence in many arrays of an array set allows the probe-sensitivity index to be estimated accurately. We extended the model to obtain expression values for PM-only arrays, and found that the 20-probe PM-only model is comparable to the 10-probe PM/MM difference model, in terms of the expression correlations with the original 20-probe PM/MM difference model. MBEI method is able to extend the reliable detection limit of expression to a lower mRNA concentration. The standard errors of MBEI can be used to construct confidence intervals of fold changes, and the lower confidence bound of fold change is a better ranking statistic for filtering genes. We can assign reliability indexes for genes in a specific cluster of interest in hierarchical clustering by resampling clustering trees. A software dChip implementing many of these analysis methods is made available. Conclusions: The model-based approach reduces the variability of low expression estimates, and provides a natural method of calculating expression values for PM-only arrays. The standard errors attached to expression values can be used to assess the reliability of downstream analysis.
Published Version: http://genomebiology.com/2001/2/8/research/0032
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC55329/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4595189
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