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Computational genetics

Computational analysis of microarray data

Key Points

  • The completion of the sequencing of a large number of prokaryotic and eukaryotic genomes presents several challenges and opportunities, including the functional classification of predicted genes.

  • Microarray analysis promises to contribute to the functional annotation of genomes and has already provided a wealth of genome-wide expression data.

  • Much attention has been focused on experimental protocols for microarray studies, but the strategies for data analysis have a profound (and perhaps underappreciated) effect on the interpretation of the results.

  • Expression data from each experiment must first be normalized to account for systematic experimental variation, including unequal dye incorporation and detection efficiencies.

  • For comparison between experiments, data is often first filtered to select a subset or to exclude genes for which there is much missing data. A distance metric must then be chosen, which determines how we measure similarity between gene-expression patterns. Genes and experiments can then be grouped using various computational methods. Each step can influence how the expression data are grouped.

  • Clustering algorithms, which are the most widely used approaches to analysing gene expression, can be classified as hierarchical or non-hierarchical (self-organizing maps (SOMs), k-means clustering and principal component analysis), agglomerative (hierarchical) or divisive (k-means, SOMs), and supervised (support vector machine) or non-supervised (hierarchical and k-means clustering, SOMs).

  • A synthetic data set with well-defined relationships between genes is used to show the differences between some of these methods.

  • The choice of data analysis strategy should be influenced by the purpose of the microarray experiment, and the user's knowledge of the biology of the system under investigation.


Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis.

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Figure 1: Data normalization.
Figure 2: A synthetic gene-expression data set.
Figure 3: Hierarchical clustering.
Figure 4: Principal component analysis.
Figure 5: The effect of data filtering.


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Cluster analysis was done using the The Institute for Genomic Research MeV software package developed by A. Sturn, A. I. Saeed and J.Q., which is available at, along with the sample data set used here. The author also thanks A. Sturn, N. H. Lee, R. L. Malek and E. Snesrud for valuable discussions and comments. This work is supported by grants from the US National Science Foundation, the US National Cancer Institute, and the US National Heart, Lung, and Blood Institute.

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TIGR Gene Indices






Imaging Research

NHGRI Microarray Project

TIGR software tools

Eisen lab



European Bioinformatics Institute (EBI) Expression Profiler

Eisen lab

Silicon Genetics


X Cluster

TIGR software tools




National Center for Genome Resources


École Normale Supérieure




The term 'cluster analysis' actually encompasses several different classification algorithms that can be used to develop taxonomies (typically as part of exploratory data analysis). Note that in this classification, the higher the level of aggregation, the less similar are members in the respective class.


The centroid of a cluster is the weighted average point in the multidimensional space; in a sense, it is the centre of gravity for the respective cluster.


A branching 'tree' diagram representing a hierarchy of categories on the basis of degree of similarity or number of shared characteristics, especially in biological taxonomy. The results of hierarchical clustering are presented as dendrograms, in which the distance along the tree from one element to the next represents their relative degree of similarity.


Neural networks are analytic techniques modelled after the (proposed) processes of learning in cognitive systems and the neurological functions of the brain. Neural networks use a data 'training set' to build rules capable of making predictions or classifications on data sets.


Factor analysis is a data reduction and exploratory method similar to pincipal component analysis. Factor analysis techniques seek to reduce the number of variables and to detect structure in the relationships between elements in an analysis.


Like principal component analysis, principal coordinate analysis seeks to reduce the dimensionality of a spatial representation of a data set by creating new coordinate axes that are a combination of the originals, and projecting the data onto those new axes.


A hyperplane is an N-dimensional analogy of a line or plane, which divides an 'N + 1' dimensional space into two.


In support vector machines, the kernel function is a generalization of the distance metric; it measures the distance between two expression vectors as the data are projected into higher-dimensional space.

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Quackenbush, J. Computational analysis of microarray data . Nat Rev Genet 2, 418–427 (2001).

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