TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.

Area of Science:

• Computational Biology
• Genomics
• Developmental Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular heterogeneity during development.
• Accurate inference of cell differentiation trajectories from scRNA-seq data remains a significant computational challenge.

Purpose of the Study:

• To develop a novel computational method for robust trajectory inference from scRNA-seq data.
• To accurately describe and model cell population heterogeneity and differentiation pathways.

Main Methods:

• Developed trajectory inference method-based probability distribution (TIPD) integrating signaling entropy and gene expression clustering.
• Proposed a novel distance metric for probability distributions of cell states.
• Constructed a minimum spanning tree (MST) on the distance matrix to infer trajectory order.

Main Results:

• TIPD effectively models cell population heterogeneity using probability distributions.
• The method successfully reconstructed known differentiation trajectories on linear and branched datasets.
• TIPD demonstrated superior performance compared to four state-of-the-art methods across various assessment criteria.

Conclusions:

• TIPD provides an accurate and versatile approach for reconstructing cell differentiation trajectories from scRNA-seq data.
• The method's ability to infer trajectories without external annotations broadens its applicability.
• TIPD advances the computational analysis of developmental processes using single-cell genomics.