seuratclustering

What problem does it solve?

Clustering single-cell RNA-seq data to identify distinct cell populations and guide downstream annotation, enabling researchers to interpret cellular heterogeneity efficiently.

Core Features & Use Cases

• Unsupervised clustering using Seurat's FindNeighbors and FindClusters with Leiden or Louvain algorithms.
• Multi-resolution exploration to balance granularity and biological relevance.
• UMAP visualization for intuitive interpretation and exploration of cluster structure.
• Real-world workflows: after QC/normalization; when integrating data with integrated reductions; and for reference-based or annotation-driven analyses.

Quick Start

Provide a Seurat object as input (SeuratPreparing) and run SeuratClustering with default parameters.