Identification of differentially expressed genes in microarray data in a principal component space

Microarray experiments are often conducted in order to compare gene expression between two conditions. Tests to detected mean differential expression of genes between conditions are conducted applying correction for multiple testing. Seldom, relationships between gene expression and microarray condi...

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Detalles Bibliográficos
Autores principales: Ospina, Luis, López-Kleine, Liliana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing AG 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604593/
https://www.ncbi.nlm.nih.gov/pubmed/23539565
http://dx.doi.org/10.1186/2193-1801-2-60
Descripción
Sumario:Microarray experiments are often conducted in order to compare gene expression between two conditions. Tests to detected mean differential expression of genes between conditions are conducted applying correction for multiple testing. Seldom, relationships between gene expression and microarray conditions are investigated in a multivariate approach. Here we propose determining the relationship between genes and conditions using a Principal Component Analysis (PCA) space and classifying genes to one of two biological conditions based on their position relative to a direction on the PC space representing each condition.