Sunday, April 28, 2024
Mitochondrial Health

Single-cell Trajectory analysis using Monocle3 and Seurat | Step-by-step tutorial



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:
https://github.com/kpatel427/YouTubeTutorials/blob/main/TI_monocle3.R

Data:
http://scrna.sklehabc.com/

Publication associated with the data:
https://academic.oup.com/nsr/article/8/3/nwaa180/5896476?login=false

Monocle3 tutorial:
https://cole-trapnell-lab.github.io/monocle3/docs/trajectories/

R package collection for Trajectory Inference:
https://dynverse.org/

Publication comparing various Trajectory Inference methods:
https://www.biorxiv.org/content/10.1101/276907v1.full.pdf

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:
https://www.buymeacoffee.com/bioinformagic

To get in touch:
Website: https://khushbupatel.info/
Github: https://github.com/kpatel427
Email: [email protected]

#bioinformagician #bioinformatics #monocle3 #monocle #trajectoryinference #trajectory #singlecell #deg #seurat #integration#R #genomics #beginners #tutorial #howto ##research #omics #biology #ncbi #rnaseq #ngs

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