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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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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. |
format | Online Article Text |
id | pubmed-5040970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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