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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessFeature PaperArticle

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories

Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai 40076, India
School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Author to whom correspondence should be addressed.
Academic Editor: Mario Cannataro
Microarrays 2016, 5(3), 23;
Received: 26 July 2016 / Revised: 6 September 2016 / Accepted: 13 September 2016 / Published: 19 September 2016
(This article belongs to the Special Issue Next Generation Microarray Bioinformatics)
PDF [946 KB, uploaded 19 September 2016]


Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at View Full-Text
Keywords: microarray; gene networks; Arabidopsis thaliana microarray; gene networks; Arabidopsis thaliana

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chockalingam, S.; Aluru, M.; Aluru, S. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories. Microarrays 2016, 5, 23.

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