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

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

bioinformagician via YouTube Direct link

Intro

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1 of 22

Intro

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

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

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