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PubMed · 11867082

A systematic statistical linear modeling approach to oligonucleotide array experiments.

Tzu Ming Chu·Bruce Weir·Russ Wolfinger
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Abstract

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.

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Cells, CulturedHumansModels, StatisticalOligonucleotide Array Sequence AnalysisRadiation, IonizingTranscription, GeneticJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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Tzu Ming Chu, Bruce Weir, Russ Wolfinger. 2002. A systematic statistical linear modeling approach to oligonucleotide array experiments.. https://doi.org/10.1016/s0025-5564(01)00107-9

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