Next Article in Journal / Special Issue
OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes
Previous Article in Journal
A Combinatorial Protein Microarray for Probing Materials Interaction with Pancreatic Islet Cell Populations
Article Menu

Export Article

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

Open AccessFeature PaperArticle
Microarrays 2016, 5(3), 23; doi:10.3390/microarrays5030023

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
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)
View Full-Text   |   Download PDF [946 KB, uploaded 19 September 2016]   |  

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Microarrays EISSN 2076-3905 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top