ArticleBioinformatics (Oxford, England)2024
SCMeTA: a pipeline for single-cell metabolic analysis data processing.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Living single-cell metabolomicsChemical science · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry.Nature methods · 2026Article
- CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.Analytical chemistry · 2026Article
- MMEASE: enhanced analytical workflow for single-cell metabolomics.Nucleic acids research · 2025Article
- Integrative MultiOmics and Machine Learning Reveal Peroxiredoxin 4 as a Critical Hub Governing Mitochondrial Dysfunction and B Cell Differentiation in Periodontitis.Clinical, cosmetic and investigational dentistry · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
summaryTo address the challenges in single-cell metabolomics (SCM) research, we have developed an open-source Python-based modular library, named SCMeTA, for SCM data processing. We designed standardized pipeline and inter-container communication format and have developed modular components to adapt to the diverse needs of SCM studies. The validation was carried out on multiple SCM experiment data. The results demonstrated significant improvements in batch effects, accuracy of results, metabolic extraction rate, cell matching rate, as well as processing speed. This library is of great significance in advancing the practical application of SCM analysis and makes a foundation for wide-scale adoption in biological studies. AVAILABILITY AND IMPLEMENTATION: SCMeTA is freely available on https://github.com/SCMeTA/SCMeTA and https://doi.org/10.5281/zenodo.13569643.
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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.