scMaSigPro: differential expression analysis along single-cell trajectories

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. 7
• Main Authors: Srivastava, Priyansh, Benegas Coll, Marta, Götz, Stefan, Nueda, María José, Conesa, Ana
• Format: Journal Article
• Language: English
• Published: Oxford University Press 08.07.2024
• Subjects: Original Paper
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btae443

Abstract Motivation Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. Results We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. Availability and implementation scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at ‘github.com/BioBam/scMaSigPro’ and archived with version 0.03 on Zenodo at ‘zenodo.org/records/12568922’.