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PreprintCC BYbioRxiv

A Unified Framework for Batch Correction and Imputation in Modern Mass Spectrometry Proteomics

  • A. M. Anwar1
  • S. Bayoumi
  • L. Lahti
  • E. Coffey
  1. 1Turku Bioscience Centre, University of Turku and Abo Akademi University, Turku 20520, Finland
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Abstract

Mass spectrometry-based proteomics, including single-cell applications, are often confounded by batch effects, inter-laboratory variation, and signal drift that can obscure biological interpretation. NMFBatch addresses these sources of unwanted variation through a unified framework that combines non-negative matrix factorization with generalized additive modelling to jointly capture discrete and continuous technical effects from protein expression data. It handles missing values directly through masking and on-the-fly imputation. Benchmarking across reference datasets, plasma proteomics cohorts, simulations, and single-cell data demonstrated that NMFBatch reduced technical variation while preserving biological structure. These results establish NMFBatch as a robust framework for batch correction, harmonization, and integrative proteomics analysis from protein expression data.

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Cite

BibTeXRIS
BibTeX
@misc{anwar2026unified,
  title = {{A Unified Framework for Batch Correction and Imputation in Modern Mass Spectrometry Proteomics}},
  author = {A. M. Anwar and S. Bayoumi and L. Lahti and E. Coffey},
  howpublished = {bioRxiv},
  year = {2026},
  month = may,
  doi = {10.64898/2026.05.19.726178},
  url = {https://www.biorxiv.org/content/10.64898/2026.05.19.726178v3},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - A Unified Framework for Batch Correction and Imputation in Modern Mass Spectrometry Proteomics
AU  - A. M. Anwar
AU  - S. Bayoumi
AU  - L. Lahti
AU  - E. Coffey
PY  - 2026
DA  - 2026/05/19
PB  - bioRxiv
DO  - 10.64898/2026.05.19.726178
UR  - https://www.biorxiv.org/content/10.64898/2026.05.19.726178v3
AB  - Mass spectrometry-based proteomics, including single-cell applications, are often confounded by batch effects, inter-laboratory variation, and signal drift that can obscure biological interpretation. NMFBatch addresses these sources of unwanted variation through a unified framework that combines non-negative matrix factorization with generalized additive modelling to jointly capture discrete and continuous technical effects from protein expression data. It handles missing values directly through masking and on-the-fly imputation. Benchmarking across reference datasets, plasma proteomics cohorts, simulations, and single-cell data demonstrated that NMFBatch reduced technical variation while preserving biological structure. These results establish NMFBatch as a robust framework for batch correction, harmonization, and integrative proteomics analysis from protein expression data.
ER  -