Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions o...

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Detalles Bibliográficos
Autores principales: Oshlack, Alicia, Emslie, Dianne, Corcoran, Lynn M, Smyth, Gordon K
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839120/
https://www.ncbi.nlm.nih.gov/pubmed/17204140
http://dx.doi.org/10.1186/gb-2007-8-1-r2
Descripción
Sumario:Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions of lowess normalization fail. Specifically, this can occur when specialized boutique arrays are constructed that contain a subset of genes selected to test particular biological functions.

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