scrnaseq-clustering

name	scrnaseq-clustering
description	Normalization, highly variable gene selection, PCA, UMAP, and Leiden/Louvain clustering of single-cell RNA-seq data.

Skill: scRNA-seq Clustering

Use When

• User has a QC-filtered count matrix and wants to cluster cells into groups.
• User wants to generate UMAP or tSNE embeddings for visualization.
• User needs to perform dimensionality reduction before cell type annotation or differential expression.

Inputs

• Required:
  • Filtered AnnData (.h5ad) or Seurat object (.rds)
• Optional:
  • Framework: scanpy or seurat (default: scanpy)
  • Normalization method: lognorm or scttransform (default: lognorm)
  • Number of highly variable genes (default: 3000)
  • Number of PCs to compute (default: 50)
  • Number of PCs to use for neighbor graph (default: 30; refine with elbow plot)
  • Number of neighbors for kNN graph (default: 15)
  • Clustering resolution (default: 0.5; range 0.1–2.0)
  • Clustering algorithm: leiden or louvain (default: leiden)
  • Variables to regress out (default: none; options: nCount_RNA, percent.mt)
  • Random seed (default: 42)

Workflow

1. Normalize counts: CPM normalization (counts per 10,000) followed by log1p transformation (sc.pp.normalize_total + sc.pp.log1p in Scanpy; NormalizeData in Seurat), or run SCTransform (Seurat v5 / scran for Scanpy).
2. Select highly variable genes (HVGs): use seurat_v3 flavor in Scanpy or FindVariableFeatures in Seurat; default to top 3000 HVGs.
3. Scale gene expression to zero mean and unit variance (sc.pp.scale or ScaleData). If regression is requested, regress out nCount_RNA and/or percent.mt during scaling. Skip scaling for SCTransform workflows.
4. Run PCA on the HVG-scaled matrix (sc.tl.pca or RunPCA); compute 50 PCs. Plot an elbow plot to identify where variance plateaus and select the number of PCs for the neighbor graph.
5. Compute the k-nearest neighbor (kNN) graph in PCA space (sc.pp.neighbors or FindNeighbors) using the selected number of PCs.
6. Cluster cells with the Leiden algorithm (sc.tl.leiden) or Louvain (sc.tl.louvain / FindClusters). Test at least two resolutions (e.g., 0.3 and 0.8) and compare cluster stability.
7. Compute UMAP embedding (sc.tl.umap or RunUMAP); optionally compute tSNE. Always set a fixed random seed.
8. Plot UMAP colored by: cluster ID, QC metrics (nFeature_RNA, nCount_RNA, percent.mt), and sample of origin. Flag clusters with elevated QC metrics as potentially low-quality.
9. Save updated AnnData (.h5ad) or Seurat object (.rds) with embeddings and cluster labels.

Output Contract

• Updated AnnData (.h5ad) or Seurat object (.rds) with cluster labels (leiden or seurat_clusters column) and UMAP/tSNE coordinates
• UMAP plots colored by cluster, QC metrics, and sample (PDF)
• PCA elbow plot (PDF)
• Cluster membership table (TSV): cell barcode, cluster ID, UMAP1, UMAP2

Limits

• Leiden resolution parameter strongly controls granularity: lower values (0.1–0.3) yield fewer, broader clusters; higher values (1.0–2.0) yield many fine-grained clusters.
• Scaling with regression is incompatible with SCTransform; choose one normalization path.
• UMAP is non-deterministic without a fixed random seed; always set random_state=42.
• Cell cycle effects may separate proliferating cells into their own cluster regardless of lineage; regress out cell cycle scores if this is not the biology of interest (sc.tl.score_genes_cell_cycle).
• SCTransform is computationally expensive for datasets with more than 100,000 cells; use lognorm for very large datasets.
• Neighbor graph quality depends on the number of PCs chosen; too few PCs lose biological signal, too many add noise. Use the elbow plot as a guide.
• Common failure cases:
  • Leiden clustering producing a single cluster (resolution too low) or as many clusters as cells (resolution too high); adjust resolution.
  • UMAP looks identical across runs due to missing random seed; check random_state parameter.
  • SCTransform failing due to insufficient memory on large datasets (>200k cells); fall back to lognorm.