New study of Turku Bioscience evaluates tools to detect longitudinal differential expression in proteomics data and introduces a robust approach called RolDE for longitudinal biomarker discovery.

Proteins are a large group of essential biomolecules providing valuable information into different disease states and processes. Mass spectrometry (MS)-based proteomics has developed significantly in recent years and emerged as a powerful tool for the discovery of protein biomarkers. A popular approach for discovering biomarker candidates is detecting differentially expressed proteins between the conditions of interest.

Longitudinal study designs, which provide more statistical power and valuable information concerning the changes in expression over time, are gaining popularity in proteomics studies. However, the data produced by mass spectrometers often include a considerable amount of technical noise and incomplete measurements, which can bias the true underlying biological signal and make it more difficult to detect correctly.

To facilitate the selection of a suitable method for the discovery of differential expression from longitudinal proteomics data, researchers from Turku Bioscience have performed a comprehensive evaluation of multiple existing differential expression methods for high-throughput longitudinal omics data and introduce a new simple-to-use robust reproducibility optimization approach RolDE.

While several of the methods performed well in the comparisons, the new RolDE method performed overall best. It was most tolerant to missing values, displayed good reproducibility and was the top method in ranking the results in a biologically meaningful way. RolDE is suitable for the detection of longitudinal differential expression in proteomics data even when the timepoints in the data are not aligned between the conditions and can easily be applied by non-experienced users.
The study was published in Nature Communications. The RolDE method is freely available as an R package in Bioconductor, making it easily accessible for researchers to use and apply in their own proteomics studies.

Välikangas, T., Suomi, T., Chandler, C.E., Scott, A.J., Tran, B.Q., Ernst, R.K., Goodlett, D.R., Elo, L.E. Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach. Nat Commun 13, 7877 (2022). https://doi.org/10.1038/s41467-022-35564-z

More information:

Laura Elo, PhD, professor
laura.elo@utu.fi, tel. +358504680795