Background#R##N#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.
['Eva Freyhult', 'Mattias Landfors', 'Jenny Önskog', 'Torgeir R. Hvidsten', 'Patrik Rydén']
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering