Single-Cell RNA-seq Normalization in Seurat | LogNormalize Step by Step

After removing low-quality cells during quality control, the next essential step in single-cell RNA-seq analysis is data normalization.

In this video (Episode 3 of the Single-Cell RNA-seq Series using Seurat), we focus on how normalization works in Seurat and why it is important before downstream analysis.

Topics covered in this video:
- Why normalization is required in single-cell RNA-seq data
- Global-scaling normalization used by Seurat
- Understanding the LogNormalize method
- How feature expression is normalized per cell
- Role of scale factor in normalization
- Where normalized data is stored in Seurat v5

We use Seurat’s default normalization workflow:
- Each cell is normalized by its total expression
- Values are multiplied by a scale factor of 10,000
- Log transformation is applied to the normalized values

We also demonstrate:
- Using NormalizeData with default parameters
- How normalized values are stored in pbmc RNA data slot in Seurat v5

While LogNormalize is widely used and considered a standard approach in scRNA-seq analysis, it relies on the assumption that each cell contains a similar amount of RNA. We briefly discuss why alternative normalization strategies exist and when they may be needed.

This video builds the foundation for downstream steps such as scaling, dimensionality reduction, and clustering.

Subscribe to the channel for upcoming videos in this single-cell RNA-seq analysis series.

Видео Single-Cell RNA-seq Normalization in Seurat | LogNormalize Step by Step канала BioinfQuests