ArticleScientific reports2019
New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition.Science advances · 2022Trial
- Effects of a short-term cold exposure on circulating microRNAs and metabolic parameters in healthy adult subjects.Journal of cellular and molecular medicine · 2022Trial
- Integration of Transcriptional Signatures from Brain Tissue and Plasma Extracellular Vesicles of a Preclinical Tauopathy Mouse Model.International journal of molecular sciences · 2026Article
- Computational approaches for identifications of altered ion channels in keratoconus.Eye (London, England) · 2025Article
- Physiological responses to a changing winter climate in an early spring-breeding amphibian.Ecology and evolution · 2024Article
- CorDiffViz: an R package for visualizing multi-omics differential correlation networks.BMC bioinformatics · 2021Article
- NetCoMi: network construction and comparison for microbiome data in R.Briefings in bioinformatics · 2021Article
- Network Analysis to Identify Communities Among Multiple Exposure Biomarkers Measured at Birth in Three Flemish General Population Samples.Frontiers in public health · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fisher's Z-transformation test is usually used. However, the Fisher's Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fisher's Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.
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Registered trials
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