Single-cell RNA sequencing tutorial set - obtaining data and pre-processing 04

🎥 A detailed guide on single-cell RNA sequencing (scRNA-seq) data analysis in MATLAB. 💻 This tutorial walks you through the entire workflow, from downloading raw data from the Gene Expression Omnibus (GEO) to sophisticated visualization and cell type identification.

Paper to get GEO from utilized samples:
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis: https://tinyurl.com/4jauf8f7
- Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer: https://tinyurl.com/ck7eznj7

Key Sections:
Data Acquisition: Learn how to download and import single-cell data from the GEO database directly into your MATLAB environment. 📥

Quality Control (QC): We'll dive into the critical step of QC, demonstrating how to filter out low-quality cells and data artifacts. I'll show you how to generate violin plots to visualize key QC metrics and explain why sample-specific QC thresholds are crucial for accurate analysis.

Dimensionality Reduction: See how to use Uniform Manifold Approximation and Projection (UMAP) to reduce high-dimensional gene expression data into a 2D representation. I'll compare the UMAP plots generated with all genes versus those created with highly variable genes, highlighting the benefits of the latter for better data visualization. 🧬

Clustering and Cell Type Identification: The tutorial concludes by demonstrating k-means clustering on the UMAP 2D coordinates to group cells with similar expression profiles. We'll then assign biological identities to these clusters using known gene markers from the PanglaoDB database. 🔬

This video is perfect for researchers and students looking to perform comprehensive single-cell data analysis using MATLAB. 👩‍🔬👨‍🔬

Видео Single-cell RNA sequencing tutorial set - obtaining data and pre-processing 04 канала Selim Romero Teaching