Manon Martin Apr 2026

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

: 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. manon martin

While her focus is statistical, her work is applied across diverse scientific areas: To ensure her theoretical work is accessible to

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. Application Domains : She has compared and enhanced

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

: 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.

: 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

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

: 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.

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

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.

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

: 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.

: 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

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