A detailed walk-though of steps to perform trajectory analysis using Monocle3 + Seurat for single-cell RNA-Seq data. In this video I cover various aspects of trajectory analysis including what is trajectory analysis, when to perform this analysis, which trajectory inference method to choose and how to perform trajectory analysis. In addition, I go over workflow steps and talk about cell data set object which monocle3 requires and finally demonstrate this workflow step-by-step in R.
I hope you find the video informative. I look forward to your comments in the comments section!
Link to code:
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Data:
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Alternate Data Link:
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Publication associated with the data:
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Monocle3 tutorial:
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R package collection for Trajectory Inference:
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Publication comparing various Trajectory Inference methods:
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Chapters:
0:00 Intro
0:52 WHAT is Trajectory analysis?
2:11 What is pseudotime?
2:43 WHEN to perform trajectory analysis?
3:57 WHICH trajectory inference method to choose?
5:52 HOW to perform trajectory analysis? - Workflow steps
7:04 cell_data_set class
8:29 Data for demo
9:20 Fetching the data
9:47 Load libraries and read data in R
12:39 Create Seurat object
16:39 Subset Seurat object to only retain B cells
19:53 Processing steps in Seurat (NormalizeData, ScaleData, RunPCA, RunUMAP and FindClusters)
25:25 Convert Seurat object to object of cell_data_set class
26:06 Retrieving data from cds object
28:11 Transfer clustering information from Seurat object to cds object
33:37 Visualize clustering using monocle3: plot_cells()
36:44 Learn trajectory graph: learn_graph()
39:06 Order cells in pseudotime: order_cells()
41:00 Plotting pseudotime for cell types in ggplot2
43:43 Find genes that change expression along a trajectory: graph_test()
47:03 Visualizing pseudotime in Seurat's FeaturePlot()
Show your support and encouragement by buying me a coffee:
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To get in touch:
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Github: [ Ссылка ]
Email: khushbu_p@hotmail.com
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