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

Eva Freyhult,Jenny Önskog,Torgeir R Hvidsten,Mattias Landfors,Patrik Rydén

doi:10.1186/1471-2105-11-503

Eva Freyhult, Jenny Önskog + Show 3 more

Open Access

https://doi.org/10.1186/1471-2105-11-503

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Journal: BMC bioinformatics	Publication Date: Oct 11, 2010
Citations: 93	License type: CC BY 2.0

Affiliation: Umeå University, Umeå Plant Science Centre

Abstract

BackgroundCluster 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.ResultWe 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 selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.ConclusionsThe choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.

Highlights

Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data
The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles
Clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data

Summary

Introduction

In particular hierarchical clustering, is widely used to extract information from gene expression data. A few studies have evaluated different clustering methods and similarity metrics on real-world microarray data. Two other studies report model-based clustering as one of the best choice for gene clustering [3,4], while yet another study found that performance varied too much between different evaluation criteria to be able to decide on one best method [5]. One reason might be that these comparative studies did not consider many of the other analysis choices that inevitably have to be made in a complete microarray experiment Both pre-processing of microarray data (normalization, missing value imputation, standardization) and gene (feature) selection is almost always performed before clustering and the choice of these methods can have dramatic effects on the final clustering results. The importance of gene selection is not surprising given the impossibility of successful classification when discriminatory genes make up a small fraction of the total number of genes considered [10]

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