Single-Cell Trajectory Analysis Using Monocle3 and Seurat - Step-by-Step Tutorial

Learn how to perform trajectory analysis for single-cell RNA-Seq data using Monocle3 and Seurat in this comprehensive 49-minute tutorial. Explore the concepts of trajectory analysis and pseudotime, understand when to apply this technique, and discover how to choose the appropriate trajectory inference method. Follow a step-by-step workflow in R, covering data preparation, Seurat object creation, cell subset selection, and essential processing steps. Convert Seurat objects to cell_data_set class, visualize clustering, learn trajectory graphs, order cells in pseudotime, and identify genes with changing expression along trajectories. Gain practical skills in data manipulation, visualization techniques, and interpretation of results using tools like ggplot2 and Seurat's FeaturePlot.

Intro
WHAT is Trajectory analysis?
What is pseudotime?
WHEN to perform trajectory analysis?
WHICH trajectory inference method to choose?
HOW to perform trajectory analysis? - Workflow steps
cell_data_set class
Data for demo
Fetching the data
Load libraries and read data in R
Create Seurat object
Subset Seurat object to only retain B cells
Processing steps in Seurat NormalizeData, ScaleData, RunPCA, RunUMAP and FindClusters
Convert Seurat object to object of cell_data_set class
Retrieving data from cds object
Transfer clustering information from Seurat object to cds object
Visualize clustering using monocle3: plot_cells
Learn trajectory graph: learn_graph
Order cells in pseudotime: order_cells
Plotting pseudotime for cell types in ggplot2
Find genes that change expression along a trajectory: graph_test
Visualizing pseudotime in Seurat's FeaturePlot