Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data

作者：Jinming Cheng, Gordon K. Smyth, Yunshun Chen · 发表于：F1000Research · 年份：2023 · DOI：10.12688/f1000research.134078.1 · 被引用次数：20 · 研究领域：Single-cell and spatial transcriptomics、Gene expression and cancer classification、Cancer-related molecular mechanisms research

Background: 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. Methods: This article demonstrates 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. Results: 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. Conclusions: The demonstrated workflow provides a valuable resource for...