Robust estimators for expression analysis.

Earl Hubbell; Wei-Min Liu; Rui Mei

doi:10.1093/bioinformatics/18.12.1585

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Robust estimators for expression analysis.

Earl Hubbell·Wei-Min Liu·Rui Mei

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Abstract

MOTIVATION: We consider the problem of estimating values associated with gene expression from oligonucleotide arrays. Such estimates should linearly track concentration, yield non-negative results, have statistical guarantees of robustness against outliers, and allow estimates of significance and variance. RESULTS: A hierarchy of simple models is used to design robust estimators meeting these goals for both stand alone and comparative experiments. This algorithm has been validated against an extensive panel of known spike experiments, and shows comparable performance to existing standards.

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Algorithms DNA Probes Gene Expression Gene Expression Profiling Gene Expression Regulation Humans Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Quality Control RNA, Messenger Reproducibility of Results Sensitivity and Specificity Stochastic Processes Yeasts Comparative Study Evaluation Study Journal Article Validation Study

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Earl Hubbell, Wei-Min Liu, Rui Mei. 2002. Robust estimators for expression analysis.. https://doi.org/10.1093/bioinformatics%2F18.12.1585

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