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Updated: Sep 12, 2025

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
Published on: April 18, 2025
scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data
Christophe Vanderaa1, Laurent Gatto2
1Computational Biology and Bioinformatics Unit (CBIO), de Duve Institute, UCLouvain, Av. Hippocrate 75, 1200, Brussels, Belgium.
None:
Analyzing mass spectrometry (MS)-based single-cell proteomics (SCP) data faces important challenges inherent to MS-based technologies and single-cell experiments. We present scplainer, a principled and standardized approach for extracting meaningful insights from SCP data using minimal data processing and linear modeling. scplainer performs variance analysis, differential abundance analysis, and component analysis while streamlining result visualization. scplainer effectively corrects for technical variability, enabling the integration of data sets from different SCP experiments. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.
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