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PreprintCC BY-NC-NDChemRxiv

TLQ Method: A Tandem Mixing-Linear Deduction-Quantile Weighted Median Framework for Robust Differential Metabolite Screening in LCMS-Based Untargeted Metabolomics

  • Teng Teng1,2
  • Yuchun Lu1,2
  • Tonglei Qiao1,2
  • Feng Liu1,2,3
  • Chun-E Liu1,2,3
  1. 1Ocean College
  2. 2Yantai Institute of China Agricultural University
  3. 3Shandong Key Laboratory of Digital Fishery
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Abstract

Untargeted metabolomics has long been constrained by three interrelated factors: inter-individual variability, difficulty in detecting low-abundance metabolites, and matrix effects. Existing data-processing strategies tend to compensate for systematic variations through algorithmic corrections after data acquisition, but their effectiveness is limited when biological variation is substantial or signals approach detection limits. To address these issues, we propose the TLQ method. Its first module, Tandem Mixing, suppresses matrix effects and enhances signals of low-abundance metabolites through equal‑volume mixing of paired control and treatment samples; its second module, Linear Deduction, strips background through linear subtraction under minimized interference, thereby recovering the true treatment signal; its third module, Quantile Quality‑Weighted Median, performs robust screening on fold‑change data using a chromatographic peak quality-driven weighted median, and further evaluates the reliability of the fold-change direction with Bootstrap confidence intervals. The three modules can be flexibly deployed—the paired mode is optimal when combined, while unpaired scenarios can adopt the A_M mode, and the third module can be independently applied to existing data to improve screening robustness. A validation experiment based on an LPS-induced mouse inflammation model is currently ongoing. Overall, the TLQ method provides an alternative route for differential metabolite screening that compresses inter-individual variability, low-abundance detection difficulty, and matrix effects at the experimental design stage.

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Cite

BibTeXRIS
BibTeX
@misc{teng2026method,
  title = {{TLQ Method: A Tandem Mixing-Linear Deduction-Quantile Weighted Median Framework for Robust Differential Metabolite Screening in LCMS-Based Untargeted Metabolomics}},
  author = {Teng Teng and Yuchun Lu and Tonglei Qiao and Feng Liu and Chun-E Liu},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15008298/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008298/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - TLQ Method: A Tandem Mixing-Linear Deduction-Quantile Weighted Median Framework for Robust Differential Metabolite Screening in LCMS-Based Untargeted Metabolomics
AU  - Teng Teng
AU  - Yuchun Lu
AU  - Tonglei Qiao
AU  - Feng Liu
AU  - Chun-E Liu
PY  - 2026
DA  - 2026/09/04
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15008298/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008298/v1
AB  - Untargeted metabolomics has long been constrained by three interrelated factors: inter-individual variability, difficulty in detecting low-abundance metabolites, and matrix effects. Existing data-processing strategies tend to compensate for systematic variations through algorithmic corrections after data acquisition, but their effectiveness is limited when biological variation is substantial or signals approach detection limits. To address these issues, we propose the TLQ method. Its first module, Tandem Mixing, suppresses matrix effects and enhances signals of low-abundance metabolites through equal‑volume mixing of paired control and treatment samples; its second module, Linear Deduction, strips background through linear subtraction under minimized interference, thereby recovering the true treatment signal; its third module, Quantile Quality‑Weighted Median, performs robust screening on fold‑change data using a chromatographic peak quality-driven weighted median, and further evaluates the reliability of the fold-change direction with Bootstrap confidence intervals. The three modules can be flexibly deployed—the paired mode is optimal when combined, while unpaired scenarios can adopt the A_M mode, and the third module can be independently applied to existing data to improve screening robustness. A validation experiment based on an LPS-induced mouse inflammation model is currently ongoing. Overall, the TLQ method provides an alternative route for differential metabolite screening that compresses inter-individual variability, low-abundance detection difficulty, and matrix effects at the experimental design stage.
ER  -