ArticleBioinformatics (Oxford, England)2023
Signed Distance Correlation (SiDCo): an online implementation of distance correlation and partial distance correlation for data-driven network analysis.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Functional and metabolomic analyses of brown adipose tissue during cold-deacclimation reveal rapid N-acetylated amino acid adaptations.iScience · 2026Article
- Unraveling the role of polyamine metabolism in postoperative delirium: insights into biochemical mechanisms and biomarker potential.npj aging · 2026Article
- Article
- Unbiased distance correlation with sample-size-aware confidence bounds for comparative omics network analysis.Frontiers in bioinformatics · 2026Article
- DisCo P-ad: Distance-Correlation-BasedMetabolites · 2025Article
- High-dimensional spectral cytometry identifies follicular regulatory CD8Clinical & translational immunology · 2025Article
- Multivariate analyses and machine learning link sex and age with antibody responses to SARS-CoV-2 and vaccination.iScience · 2024Article
- Cystine/glutamate antiporter xCT controls skeletal muscle glutathione redox, bioenergetics and differentiation.Redox biology · 2024Article
- Navigating the archaeal frontier: insights and projections from bioinformatic pipelines.Frontiers in microbiology · 2024Article
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Authors and funding
7 authors.
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Abstract
motivationThere is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation.
resultsWe present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI platform for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables, as well as correlation between vectors of different lengths, e.g. different sample sizes. By combining the sign of the overall trend from Pearson's correlation with distance correlation values, we further provide a novel "signed distance correlation" of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement "partial distance correlation," calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset. AVAILABILITY AND IMPLEMENTATION: The SiDCo software application is freely available at https://complimet.ca/sidco. Supplementary help pages are provided at https://complimet.ca/sidco. Supplementary Material shows an example of an application of SiDCo in metabolomics.
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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.