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

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...

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Autores principales: Chockalingam, Sriram, Aluru, Maneesha, Aluru, Srinivas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040970/
https://www.ncbi.nlm.nih.gov/pubmed/27657141
http://dx.doi.org/10.3390/microarrays5030023
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author Chockalingam, Sriram
Aluru, Maneesha
Aluru, Srinivas
author_facet Chockalingam, Sriram
Aluru, Maneesha
Aluru, Srinivas
author_sort Chockalingam, Sriram
collection PubMed
description 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.
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spelling pubmed-50409702016-10-05 Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories Chockalingam, Sriram Aluru, Maneesha Aluru, Srinivas Microarrays (Basel) Article 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. MDPI 2016-09-19 /pmc/articles/PMC5040970/ /pubmed/27657141 http://dx.doi.org/10.3390/microarrays5030023 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chockalingam, Sriram
Aluru, Maneesha
Aluru, Srinivas
Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title_full Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title_fullStr Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title_full_unstemmed Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title_short Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories
title_sort microarray data processing techniques for genome-scale network inference from large public repositories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040970/
https://www.ncbi.nlm.nih.gov/pubmed/27657141
http://dx.doi.org/10.3390/microarrays5030023
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