Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data
Published in F1000 Research, 2023
In this article, we demonstrated a complete workflow of a pseudo-temporal trajectory analysis of scRNA-seq data. This workflow takes single-cell count matrices as input and leverages the Seurat pipeline for standard scRNA-seq analysis, including quality control, normalization, and integration. The scDblFinder package is utilized for doublet prediction. Trajectory inference is conducted with monocle3, while the edgeR QL framework with a pseudo-bulking strategy is applied for pseudo-time course analysis.
Recommended citation: Cheng, J., Smyth, G. K., and Chen, Y. (2023). "Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data." F1000 Research. 12.
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