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Single-Cell Trajectory Analysis Using Monocle3 and Seurat - Step-by-Step Tutorial
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- 1 Intro
- 2 WHAT is Trajectory analysis?
- 3 What is pseudotime?
- 4 WHEN to perform trajectory analysis?
- 5 WHICH trajectory inference method to choose?
- 6 HOW to perform trajectory analysis? - Workflow steps
- 7 cell_data_set class
- 8 Data for demo
- 9 Fetching the data
- 10 Load libraries and read data in R
- 11 Create Seurat object
- 12 Subset Seurat object to only retain B cells
- 13 Processing steps in Seurat NormalizeData, ScaleData, RunPCA, RunUMAP and FindClusters
- 14 Convert Seurat object to object of cell_data_set class
- 15 Retrieving data from cds object
- 16 Transfer clustering information from Seurat object to cds object
- 17 Visualize clustering using monocle3: plot_cells
- 18 Learn trajectory graph: learn_graph
- 19 Order cells in pseudotime: order_cells
- 20 Plotting pseudotime for cell types in ggplot2
- 21 Find genes that change expression along a trajectory: graph_test
- 22 Visualizing pseudotime in Seurat's FeaturePlot
