Sci Rep. 2025 May 2;15(1):15393. doi: 10.1038/s41598-025-98492-0.

ABSTRACT

Pathway analysis is a frequent step in studies involving gene or protein expression data, but most of the available pathway methods are designed for simple case versus control studies of two sample groups without further complexity. The few available methods allowing the pathway analysis of more complex study designs cannot use pathway structures or handle the situation where the variable of interest is not defined for all samples. Such scenarios are common in longitudinal studies with so long follow up time that healthy controls are required to identify the effect of normal aging apart from the effect of disease development, which is not defined for controls. To address the need, we introduce a new method for Pathway Analysis of Longitudinal data (PAL), which is suitable for complex study designs, such as longitudinal data. The main advantages of PAL are the use of pathway structures and the suitability of the approach for study settings beyond currently available tools. We demonstrate the performance of PAL with simulated data and three longitudinal datasets related to the early development of type 1 diabetes, which involve different study designs and only subtle biological signals, and include both transcriptomic and proteomic data. An R package implementing PAL is publicly available at https://github.com/elolab/PAL .

PMID:40316626 | PMC:PMC12048611 | DOI:10.1038/s41598-025-98492-0