Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses
1 Department of Epidemiology and Biostatistics, VU University Medical Center, P.O. Box 7057, Amsterdam, 1007, MB, The Netherlands
2 Department of Mathematics, VU University Amsterdam, De Boelelaan 1081a, Amsterdam, 1081, HV, The Netherlands
3 Research Unit of Radiation Cytogenetics, Helmholtz Zentrum München, Ingolstädter-Landstrasse 1, Neuherberg, 85764, Germany
4 Department of Pathology, VU University Medical Center, P.O. Box 7057, Amsterdam, 1007, MB, The Netherlands
BMC Bioinformatics 2012, 13:80 doi:10.1186/1471-2105-13-80Published: 4 May 2012
An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.
We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.
Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.