Graph-Based Clustering in Single-Cell RNA-seq | KNN, SNN & Community Detection Explained (Seurat)

Описание: 🧬 Graph-based clustering is a key step in single-cell RNA-seq analysis. After dimensionality reduction using PCA, Seurat identifies cell populations using a graph-based clustering strategy.

In this video, we explain the intuition behind K-Nearest Neighbor (KNN) graphs, Shared Nearest Neighbor (SNN) graphs, and community detection algorithms used by Seurat to identify clusters of similar cells.

📚 Topics covered in this video:

🔹 What is graph-based clustering in single-cell RNA-seq
🔹 How PCA embeddings are used to build cell–cell similarity graphs
🔹 Understanding KNN (K-Nearest Neighbor) graphs
🔹 How SNN (Shared Nearest Neighbor) improves clustering robustness
🔹 Community detection for identifying cell clusters
🔹 How Seurat performs clustering using FindNeighbors() and FindClusters()

This video is part of the Single-Cell RNA-seq Analysis using Seurat series.

📌 Series workflow:
1️⃣ Loading CellRanger data into Seurat
2️⃣ Graph-based clustering (this video)
3️⃣ UMAP & t-SNE visualization
4️⃣ Differential gene expression (marker genes)
5️⃣ Cell type annotation

If you're learning bioinformatics, computational biology, or single-cell RNA-seq analysis, consider subscribing for more tutorials.

#SingleCellRNAseq #Seurat #Bioinformatics #scRNAseq #Clustering