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Towards the uniform distribution of null P values on Affymetrix microarrays.

ABSTRACT: Methods to control false-positive rates require that P values of genes that are not differentially expressed follow a uniform distribution. Commonly used microarray statistics can generate P values that do not meet this assumption. We show that poorly characterized variance, imperfect normalization, and cross-hybridization are among the many causes of this non-uniform distribution. We demonstrate a simple technique that produces P values that are close to uniform for nondifferentially expressed genes in control datasets.

SUBMITTER: Fodor AA

PROVIDER: S-EPMC1929139 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Towards the uniform distribution of null P values on Affymetrix microarrays.

Fodor Anthony A AA Tickle Timothy L TL Richardson Christine C

Genome biology 20070101 5

Methods to control false-positive rates require that P values of genes that are not differentially expressed follow a uniform distribution. Commonly used microarray statistics can generate P values that do not meet this assumption. We show that poorly characterized variance, imperfect normalization, and cross-hybridization are among the many causes of this non-uniform distribution. We demonstrate a simple technique that produces P values that are close to uniform for nondifferentially expressed ...[more]

PMID: 17472745

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Project description:Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.

Dataset Information

Towards the uniform distribution of null P values on Affymetrix microarrays.

Publications

Towards the uniform distribution of null P values on Affymetrix microarrays.

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