How to analyze single-cell RNA-Seq data in R | Detailed Seurat Workflow Tutorial

A detailed walk-through of standard workflow steps to analyze a single-cell RNA sequencing dataset from 10X Genomics in R using the #Seurat package. I hope you liked the video. I look forward to your comments under the comments section!

Link to 10X dataset:
https://www.10xgenomics.com/resources... (Gene Expression - Feature / cell matrix HDF5 (raw))

Link to code:
https://github.com/kpatel427/YouTubeT...

Chapters:
0:00 Intro
1:52 Download data from 10X Genomics website
4:16 Read counts matrix
6:33 Create a Seurat Object
7:53 Quality Control
15:36 Filtering
16:22 Normalization
17:33 '@commands' slot
18:38 Find Variable Features
21:22 Scale data
23:51 Difference between @counts, @data and @scale.data slots
24:58 Linear dimensionality reduction (PCA)
27:35 Determine the dimensionality of the dataset
29:32 Clustering
30:33 Understanding 'Resolution' in Clustering
34:48 Non-linear dimensionality reduction (UMAP)

Show your support and encouragement by buying me a coffee:
https://www.buymeacoffee.com/bioinfor...


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

#bioinformagician #bioinformatics #seurat #R #genomics #beginners #tutorial #howto #omics #research #biology #ncbi #GEO #rnaseq #ngs