biogrowleR: Enhancing Longitudinal Data Analysis.
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Embargo End Date
ICR Authors
Authors
Ronchi, C
Ambrosini, G
Hughes, F
Flaherty, RL
Quinn, HM
Matvienko, D
Agnoletto, A
Brisken, C
Ambrosini, G
Hughes, F
Flaherty, RL
Quinn, HM
Matvienko, D
Agnoletto, A
Brisken, C
Document Type
Journal Article
Date
2025-06-03
Date Accepted
2025-05-13
Abstract
Time course measurements are used for many applications in biomedical research, ranging from growth curves to drug efficacy testing and high-throughput screening. Statistical methods used to analyze the resulting longitudinal data, such as t-tests or repeated measures ANOVA have limitations when groups are unbalanced, or individual measurements are missing. To address these issues we developed biogrowleR (https://upbri.gitlab.io/biogrowleR/), a workflow to visualize and analyze data based on Frequentist and Bayesian inference combined with hierarchical modeling. By focusing on effect sizes we enhance data interpretation. The workflow further includes a randomization algorithm important to reduce numbers of experimental animals (RRR) and costs. The workflow and R package were designed to be used by researchers with limited experience in R and biostatistics. Our open-source R package biogrowleR contains tutorials, pipelines, and helper functions for the analysis of longitudinal data and enables non computational scientists to perform more effective data analysis.
Citation
Journal of Mammary Gland Biology and Neoplasia, 2025, 30 (1), pp. 9 -
Source Title
Journal of Mammary Gland Biology and Neoplasia
Publisher
SPRINGER/PLENUM PUBLISHERS
ISSN
1083-3021
eISSN
1573-7039
Collections
Research Team
Endocrine control mechans