Unraveling the timeline of gene expression – a pseudotemporal trajectory analysis of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells.

Researchers from the Walter and Eliza Hall Institute of Medical Research demonstrate a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR.

UMAP visualization of the integrated data

Cells are coloured by cluster on the left and by original sample on the right.

Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland.

The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Availability – Source code available from: https://github.com/jinming-cheng/TimeCoursePaperWorkflow

Cheng J, Smyth GK, Chen Y. (2023) Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data. F1000Res 12:684. [article]

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