Dive into a comprehensive tutorial on analyzing single-cell RNA sequencing data using R and the Seurat package. Follow a detailed workflow for processing a 10X Genomics dataset, covering essential steps from data download to UMAP visualization. Learn to read count matrices, create Seurat objects, perform quality control, normalize data, find variable features, scale data, and understand different data slots. Explore dimensionality reduction techniques, including PCA and UMAP, and delve into clustering methods with a focus on resolution. Gain practical insights into bioinformatics analysis for genomics research, suitable for beginners and experienced researchers alike.
How to Analyze Single-Cell RNA-Seq Data in R - Detailed Seurat Workflow Tutorial
Overview
Syllabus
Intro
Download data from 10X Genomics website
Read counts matrix
Create a Seurat Object
Quality Control
Filtering
Normalization
'@commands' slot
Find Variable Features
Scale data
Difference between @counts, @data and @scale.data slots
Linear dimensionality reduction PCA
Determine the dimensionality of the dataset
Clustering
Understanding 'Resolution' in Clustering
Non-linear dimensionality reduction UMAP
Taught by
bioinformagician
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