ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is vital for studying cellular processes.
• Determining the origin (stem or progenitor cells) of cell trajectories is essential but challenging.
• Existing methods for pluripotency quantification have limitations.

Purpose of the Study:

• To introduce ORIGINS, a novel computational tool for quantifying cell pluripotency from scRNA-seq data.
• To provide a method for identifying stem and progenitor cells, enabling accurate trajectory inference.
• To offer an efficient and memory-saving alternative to existing tools.

Main Methods:

• ORIGINS utilizes a protein-protein interaction (PPI) network relevant to differentiation.
• It integrates the PPI network with gene expression data to compute a 'differentiation activity' score.
• The R package is benchmarked against LandSCENT and CytoTRACE using human datasets (breast, colon, hematopoietic, lung).

Main Results:

• ORIGINS effectively quantifies pluripotency across various human tissues.
• The tool demonstrates superior efficiency compared to LandSCENT.
• ORIGINS requires less RAM memory than comparable methods, making it more accessible.

Conclusions:

• ORIGINS provides a robust and efficient method for assessing cell pluripotency from scRNA-seq data.
• This tool facilitates accurate identification of stem cells, improving trajectory inference.
• The freely available R package offers a valuable resource for the research community.