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Open AccessFeature PaperArticle

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

1
Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai 40076, India
2
School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
3
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; https://doi.org/10.3390/microarrays5030023
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)
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 http://alurulab.cc.gatech.edu/microarray-pp. View Full-Text
Keywords: microarray; gene networks; Arabidopsis thaliana microarray; gene networks; Arabidopsis thaliana
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MDPI and ACS Style

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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