ArticlePLoS computational biology2022
AC-PCoA: Adjustment for confounding factors using principal coordinate analysis.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Novel integrase mutations linked to genotypic DTG resistance in African non-B HIV-1 strains: the DTG RESIST study.The Journal of antimicrobial chemotherapy · 2026Article
- Effect of polyphosphates on productivity and physiological characteristics in laying hens.Journal of animal science and technology · 2026Article
- Genotype-dependent stability and specialization of arbuscular mycorrhizal fungal communities under drought in common bean.Frontiers in plant science · 2026Article
- Exploring the Fecal Microbiome Dysbiosis and Its Plasma Metabolome Determinants in Advanced Parkinson's Disease With Motor Complications.CNS neuroscience & therapeutics · 2026Article
- The knowns and unknowns of phlebotomine sand flies (Diptera: Psychodidae) in selected countries of Central Europe.Parasites & vectors · 2025Review
- Novel integrase mutations linked to genotypic DTG resistance in non-B HIV-1 strains from African participants: The DTG RESIST study.medRxiv : the preprint server for health sciences · 2025Article
- Region-Specific Gut Microbiome Variation Between Changle Geese and Yellow-Feathered Broilers: Correlations with Growth and Intestinal Development.Microorganisms · 2025Article
- Oral Dysbiosis Is Associated with the Pathogenesis of Aortic Valve Diseases.Microorganisms · 2025Article
- Particle associated denitrification in coastal aquaculture water.BMC microbiology · 2025Article
- Article
- Effects of different crop rotation patterns on the growth of Guizhou sorghum and rhizosphere soil quality: implications for agricultural policy.Frontiers in microbiology · 2025Article
- Does climatic variation drive the adjustment of functional traits? An assessment of Tropical Montane Cloud Forest tree species.Frontiers in plant science · 2025Article
- GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.Journal of cellular and molecular medicine · 2024Article
- Effects of forages on the microbiota of crossed sheep on cold Plateau.Animal biotechnology · 2024Article
- PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.PLoS computational biology · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis.Experimental and therapeutic medicine · 2024Article
- Metagenomic analysis of mycobiome in wild and captivity Sumatran orangutans (Journal of advanced veterinary and animal research · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data. In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.
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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.