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

ABSTRACT:

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 microarr

SUBMITTER: Freyhult E 

PROVIDER: S-EPMC3098084 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature