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Strategies for Clustering, Classifying, Integrating, Standardizing and Visualizing Microarray Gene Expression Data

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A Beginner’s Guide to Microarrays

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Over the last century, investigation of the anatomical and morphological characteristics of a small number of organisms has played an important role in the understanding of numerous biological processes. The rediscovery of Mendel’s laws of heredity in the opening of the 20th century initiated a scientific quest to understand the mechanisms of how genetic information is transmitted and the biological consequences of genetic variation. With the emergence of molecular biology in the last thirty years, classical genetic research has shifted from understanding how visible traits are transmitted to the study of the genome structure at the molecular level. Innovations such as PCR and advances in robotics such as miniaturization and parallelization have lead to a rapid development of more accurate, sensitive and powerful devices used for the analysis of the molecular structure, function and interaction of gene products. With the development of ultrahigh throughput screening tools and the drive from 96-microwell plates to 384- and 1536-microwell plates, it is expected that the generation of whole sequences of different organisms will increase at a rate ~100 times higher than previously anticipated (HeadGordon and Wooley, 2001; Helfrich, 2002; Beson et al. 2002). As powerful, automated biological sampling and analytical tools are becoming available to more laboratories, an exponential and sometimes overwhelming accumulation of multi-format post-genomic datasets is produced. Consequently, modern biology is becoming a data driven multidisciplinary science in which biologists, mathematicians, statisticians, physicists and computer scientists are developing tools to identify ‘in silico’ the coding regions of genes, predict and model protein structural characteristics, define protein-protein interactions, construct biochemical networks and identify potential drug targets.

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  1. Department of Plant Pathology, North Dakota State University Genomics and Bioinformatics Group, USA

    Willy Valdivia Granda

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  1. University of Kentucky Medical Center, USA

    Eric M. Blalock

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Granda, W.V. (2003). Strategies for Clustering, Classifying, Integrating, Standardizing and Visualizing Microarray Gene Expression Data. In: Blalock, E.M. (eds) A Beginner’s Guide to Microarrays. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8760-0_8

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