ArticleBiomedicines2024
Correction of Batch Effect in Gut Microbiota Profiling of ASD Cohorts from Different Geographical Origins.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Traditional Foods, Oral Microbiome, and Systemic Health: Molecular Pathways Linking Nutrition and Oral Disease Prevention.International journal of molecular sciences · 2026Review
- Ecological patterns of the gut mycobiome and microbiome in ulcerative colitis across life stages.Frontiers in cellular and infection microbiology · 2026Article
- The Microbiota-Gut-Brain Axis in Autism: Associations, Causal Inference, and Interventions-A Narrative Review.Pathogens (Basel, Switzerland) · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
backgroundTo date, there have been numerous metataxonomic studies on gut microbiota (GM) profiling based on the analyses of data from public repositories. However, differences in study population and wet and dry pipelines have produced discordant results. Herein, we propose a biostatistical approach to remove these batch effects for the GM characterization in the case of autism spectrum disorders (ASDs).
methodsAn original dataset of GM profiles from patients with ASD was ecologically characterized and compared with GM public digital profiles of age-matched neurotypical controls (NCs). Also, GM data from seven case-control studies on ASD were retrieved from the NCBI platform and exploited for analysis. Hence, on each dataset, conditional quantile regression (CQR) was performed to reduce the batch effects originating from both technical and geographical confounders affecting the GM-related data. This method was further applied to the whole dataset matrix, obtained by merging all datasets. The ASD GM markers were identified by the random forest (RF) model.
resultsWe observed a different GM profile in patients with ASD compared with NC subjects. Moreover, a significant reduction of technical- and geographical-dependent batch effects in all datasets was achieved. We identified
conclusionsHerein, we proposed an updated biostatistical approach to reduce the technical and geographical batch effects that may negatively affect the description of bacterial composition in microbiota studies.
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