Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
AbstractPre-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
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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.
Chockalingam S, Aluru M, Aluru S. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories. Microarrays. 2016; 5(3):23.Chicago/Turabian Style
Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas. 2016. "Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories." Microarrays 5, no. 3: 23.
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