limpca

limpca applies a General Linear Model (GLM) version of ASCA (ANOVA-Simultaneous Component Analysis) and APCA (ANOVA-Principal Component Analysis) to analyze high-dimensional multivariate sample profiles generated by an experimental design.

Key Features:

• ASCA+ and APCA+ Methods: Implements enhanced versions of ASCA and APCA based on general linear model principles that produce unbiased estimators of factor effects in unbalanced designs.
• High-Dimensional Data Handling: Analyzes multivariate datasets with more variables than observations, unlike MANOVA.
• Visualization of Multivariate Structures: Provides powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design.
• Unbalanced Design Support: Handles unbalanced experimental designs, addressing a key limitation of the original ASCA and APCA methods.

Scientific Applications:

• Metabolomics Data Analysis: Identifies true biomarkers corresponding to effects of interest in unbalanced metabolomics experimental designs.
• Designed Experiment Analysis: Analyzes multivariate sample profiles from experimental designs in medical, biological, chemical, and agronomic fields.
• Spectral and Image Data Analysis: Processes highly multivariate data such as spectra or images where the number of variables exceeds the number of experimental units.

Methodology:

limpca implements ASCA+ and APCA+ methods based on several principles from the theory of general linear models (GLM), combining ANOVA and PCA to decompose multivariate data according to the experimental design effects.