Challenges in microarray class discovery: a comprehensive examination of normalization,gene selection and clustering |
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| Authors: | Eva Freyhult Mattias Landfors Jenny Önskog Torgeir R Hvidsten Patrik Rydén |
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| Institution: | 1.Department of Clinical Microbiology, Division of Clinical Bacteriology,Ume? University,Ume?,Sweden;2.Computational Life Science Cluster (CLiC),Ume? University,Ume?,Sweden;3.Department of Mathematics and Mathematical Statistics,Ume? University,Ume?,Sweden;4.Department of Statistics,Ume? University,Ume?,Sweden;5.Ume? Plant Science Centre, Department of Plant Physiology,Ume? University,Ume?,Sweden |
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| 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. |
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