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Finding coexpressed genes in counts-based data: an improved measure with validation experiments.

Morgan N Price·Eleanor Rieffel
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Abstract

MOTIVATION: Expressed sequence tag (EST) data reflects variation in gene expression, but previous methods for finding coexpressed genes in EST data are subject to bias and vastly overstate the statistical significance of putatively coexpressed genes. RESULTS: We introduce a new method (LNP) that reports reasonable p-values and also detects more biological relationships in human dbEST than do previous methods. In simulations with human dbEST library sizes, previous methods report p-values as low as 10(-30) on 1/1000 uncorrelated pairs, while LNP reports significance correctly. We validate the analysis on real human genes by comparing coexpressed pairs to gene ontology annotations and find that LNP is more sensitive than the three previous methods. We also find a small but statistically significant level of coexpression between interacting proteins relative to randomized controls. The LNP method is based on a log-normal prior on the distribution of expression levels.

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AlgorithmsDatabases, Nucleic AcidExpressed Sequence TagsGene Expression ProfilingHumansReproducibility of ResultsSensitivity and SpecificitySequence AlignmentSequence Analysis, DNAComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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BibTeXRIS

Morgan N Price, Eleanor Rieffel. 2004-01-29. Finding coexpressed genes in counts-based data: an improved measure with validation experiments.. https://doi.org/10.1093/bioinformatics%2Fbth011

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