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Updated: Feb 14, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform
Felix Friedrich1, Cátia Marques1, Ingela Lanekoff1,2
1Department of Chemistry for Life Sciences, Uppsala University, Uppsala 75 123, Sweden.
Single-cell metabolomics (SCM) reveals cellular differences. A new MATLAB tool, CellMate, simplifies SCM data analysis, enabling deeper insights into metabolite heterogeneity.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Computational Biology
Background:
- Single-cell metabolomics (SCM) is crucial for understanding cellular heterogeneity.
- Existing SCM data analysis tools are limited and often incompatible with conventional methods.
- High-resolving mass spectrometry enables sensitive detection of metabolites in single cells.
Purpose of the Study:
- To introduce CellMate, a MATLAB-based platform for processing single-cell metabolomics data.
- To provide a user-friendly interface for metabolite identification and peak alignment.
- To support quantitative, targeted, and untargeted metabolomic workflows.
Main Methods:
- Development of CellMate, a MATLAB platform utilizing direct infusion techniques.
- Implementation of a graphical user interface for intuitive data processing.
- Integration of a deep learning algorithm for distinguishing metabolites in untargeted workflows.
Main Results:
- CellMate facilitates metabolite identification and peak alignment for SCM data.
- The platform supports customizable quantitative, targeted, and untargeted metabolomic analyses.
- A deep learning model effectively differentiates endogenous metabolites from background noise.
Conclusions:
- CellMate enhances the analysis of single-cell metabolomics data, overcoming current limitations.
- The tool enables comprehensive extraction of metabolite information from individual cells.
- CellMate advances the capabilities of the single-cell metabolomics research toolbox.
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