Martin - Manon

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.

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

Manon Martin is a prominent researcher at the , specializing in biostatistics and the analysis of high-dimensional "omics" data. Her work primarily focuses on developing statistical frameworks and software to interpret complex experimental designs in fields like metabolomics and peptidomics. 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

: 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. The primary goal of Martin’s research is to

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

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

Below is a structured "paper" summarizing the core pillars of her scientific contributions and research focus.