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Related Experiment Video

Updated: Aug 21, 2025

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ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods.

Anders Hagen Jarmund1,2, Torfinn Støve Madssen3, Guro F Giskeødegård4

  • 1Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Frontiers in Molecular Biosciences
|November 17, 2022
PubMed
Summary

The ALASCA R package enhances biomedical research by providing accessible statistical methods for analyzing complex multivariate data. It visualizes experimental factor effects, especially for longitudinal studies with repeated measurements.

Keywords:
ASCARlongitudinal data analysismultivariate analysisomics analysisstatistical method

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Data Science

Background:

  • Biomedical research increasingly generates complex multivariate data, necessitating advanced statistical methods.
  • Existing methods struggle to effectively describe and model intricate variable relationships.
  • The ANOVA simultaneous component analysis (ASCA+) framework offers a robust approach for decomposing and visualizing experimental factor effects.

Purpose of the Study:

  • To introduce and demonstrate the ALASCA R package for applying the extended ASCA+ framework.
  • To showcase the package's utility for analyzing multivariate data in interventional and observational studies.
  • To highlight the package's capabilities for longitudinal data analysis with repeated measurements and covariate adjustment.

Main Methods:

  • The study utilizes the ALASCA package, integrating general linear models and principal component analysis (PCA).
  • It incorporates linear mixed models within the ASCA+ framework for longitudinal data (RM-ASCA+).
  • The package offers validation and visualization tools for data interpretation.

Main Results:

  • The ALASCA package provides an accessible implementation of the ASCA+ framework for R users.
  • Demonstrated successful application on diverse publicly available datasets (proteomics, metabolomics, transcriptomics).
  • Highlighted the package's effectiveness in gaining insights from complex longitudinal and observational multivariate data.

Conclusions:

  • The ALASCA package democratizes advanced statistical analysis for multivariate biomedical data.
  • It is particularly valuable for longitudinal studies, offering flexibility with covariate adjustment.
  • The package facilitates deeper understanding of complex biological systems through robust data visualization and decomposition.