RNA-based biological age clocks capture skeletal muscle aging but not sarcopenia-related transcriptomic variation
Year: 2026
Type: software
Abstract: This repository contains the R scripts and supporting template files used to reproduce the computational analyses reported in the manuscript “RNA-based biological age clocks capture skeletal muscle aging but not sarcopenia-related transcriptomic variation”. The study evaluates the performance of transcriptomic biological age clocks in human skeletal muscle RNA-seq datasets, with emphasis on chronological age prediction, sarcopenia discrimination, age-acceleration metrics, external validation, and biological interpretation across independent publicly available GEO cohorts. The deposited material includes individual R scripts for project setup, GEO processed-data handling, metadata curation, expression-matrix harmonization, exploratory quality control, PCA, clock-gene coverage assessment, transcriptomic age prediction, age-acceleration estimation, discovery analyses, logistic regression models, ROC/AUC evaluation, external validation, biological validation, table generation, and figure generation. A master script is also provided to run the full workflow in sequence. The workflow was designed to support transparency and reproducibility of the in silico analyses. Because the primary transcriptomic datasets were obtained from public repositories, this deposit does not include raw sequencing files. Users should download the corresponding GEO datasets directly from their original sources and organize them according to the directory structure described in the README file. Template files for metadata, clock-gene lists, model coefficients, and derived predictions are included to facilitate reproduction and adaptation of the pipeline. This resource is intended for researchers interested in skeletal muscle aging, sarcopenia, transcriptomic biomarkers, biological age estimation, RNA-seq analysis, and reproducible computational geroscience.
Language: English
Subfield: Aging
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7166731616