Manon: Martin

: In the field of single-cell proteomics, she contributed to scplainer , a tool using linear models to understand variation in mass spectrometry-generated peptidomics data. 3. Software Development

While her focus is statistical, her work is applied across diverse scientific areas:

: She has authored accessible guides on Linear Regression, ANOVA, and Linear Mixed Models tailored specifically for chemists and life-science researchers. 4. Application Domains manon martin

: An R package designed for the linear modeling of high-dimensional designed data based on the ASCA/APCA family.

: Martin has significantly advanced the ASCA (ANOVA-Simultaneous Component Analysis) family of methods. Her work on LiMM-PCA combines Linear Mixed Models (LMM) with Principal Component Analysis (PCA) to handle advanced designs with random effects and quantitative variables. : In the field of single-cell proteomics, she

: She has compared and enhanced techniques like AMOPLS and AComDim , extending them to unbalanced experimental designs using Generalized Linear Model (GLM) versions of matrix decomposition.

The primary goal of Martin’s research is to bridge the gap between complex experimental designs (e.g., multifactorial, longitudinal, or unbalanced designs) and the analysis of high-dimensional data, such as NMR spectra or mass spectrometry. She develops methods that allow scientists to extract meaningful biological insights from data that would otherwise be confounded by noise or complex variables. Her work on LiMM-PCA combines Linear Mixed Models

To ensure her theoretical work is accessible to the broader scientific community, Martin actively develops open-source tools: