Trajectory Inference for Single Cell Omics

TL;DR

This paper provides a comprehensive overview of trajectory inference methods in single-cell omics, discussing their concepts, strengths, weaknesses, best practices, and applications in studying cell differentiation, development, and disease.

Contribution

It offers a general introduction to trajectory inference, compares different methods, and provides guidelines for validation and interpretation in biological research.

Findings

Highlights strengths and weaknesses of various methods

Provides best practices for validation and interpretation

Showcases applications in cell differentiation and disease

Abstract

Trajectory inference is used to order single-cell omics data along a path that reflects a continuous transition between cells. This approach is useful for studying processes like cell differentiation, where a stem cell matures into a specialized cell type, or investigating state changes in pathological conditions. In the current article, we provide a general introduction to trajectory inference, explaining the concepts and assumptions underlying the different methods. We then briefly discuss the strengths and weaknesses of different trajectory inference methods. We also describe best practices for using trajectory inference, such as how to validate the results and how to interpret them in the context of biological knowledge. Finally, the article highlights some applications of trajectory inference in single-cell omics research. These applications include studying cell differentiation, development, and disease.