ArticleMicroarrays (Basel, Switzerland)2016
Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.
Article in Microarrays (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation.Genome biology · 2023Pooled it
- Combined inhibition of histone methyltransferases EZH2 and DOT1L is an effective therapy for neuroblastoma.Cancer medicine · 2024Article
- Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.Biomimetics (Basel, Switzerland) · 2023Article
- MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.Bioinformatics (Oxford, England) · 2023Article
- Long-Term Mild Heat Causes Post-Mitotic Pollen Abortion Through a Local Effect on Flowers.Frontiers in plant science · 2022Article
- Altered expression of genes controlling metabolism characterizes the tissue response to immune injury in lupus.Scientific reports · 2021Article
- Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis.Scientific reports · 2020Article
- Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease.PLoS computational biology · 2019Article
- AP2/ERF Transcription Factor Regulatory Networks in Hormone and Abiotic Stress Responses inFrontiers in plant science · 2019Review
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Authors and funding
3 authors.
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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.
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