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PLoS One. 2010 Nov 12;5(11):e13822. doi: 10.1371/journal.pone.0013822.

Cross-platform microarray data normalisation for regulatory network inference.

Author information

1
Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin, Ireland. asirbu@computing.dcu.ie

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.

PMID:
21103045
PMCID:
PMC2980467
DOI:
10.1371/journal.pone.0013822
[Indexed for MEDLINE]
Free PMC Article

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