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

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

🧬 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