Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result: We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selectio...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Eva Freyhult, Mattias Landfors, Jenny Önskog, Torgeir R. Hvidsten, Patrik Rydén
• Keywords: BMCBI 2010, Cluster Analysis, Clustering Methods, Gene Selection