ArticlePloS one2010
Cross-platform microarray data normalisation for regulatory network inference.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hippocampal gene expression meta-analysis identifies aging and age-associated spatial learning impairment (ASLI) genes and pathways.PloS one · 2013Pooled it
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression.BMC cancer · 2020Article
- Alternative empirical Bayes models for adjusting for batch effects in genomic studies.BMC bioinformatics · 2018Article
- Efficient and biologically relevant consensus strategy for Parkinson's disease gene prioritization.BMC medical genomics · 2016Article
- SBR-Blood: systems biology repository for hematopoietic cells.Nucleic acids research · 2016Article
- Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.Microarrays (Basel, Switzerland) · 2015Article
- Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia.BMC medical genomics · 2013Article
- Gene expression is highly correlated on the chromosome level in urinary bladder cancer.Cell cycle (Georgetown, Tex.) · 2013Article
- virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.BMC bioinformatics · 2013Article
- Optimal scaling of digital transcriptomes.PloS one · 2013Article
- RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.PloS one · 2012Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.
methodsWe analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.
conclusionsResults identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.