Cross-platform microarray data normalisation for regulatory network inference.
basic_science · Level V
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- Record sourced from PubMed, PMID 21103045.
- Also identified by DOI 10.1371/journal.pone.0013822 and PMC identifier PMC2804297.
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Abstract
BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences. METHODS: We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets. CONCLUSIONS: Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.