bio-single-cell-clustering

name	bio-single-cell-clustering
description	Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data.
tool_type	mixed
primary_tool	Seurat

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Single-Cell Clustering

Dimensionality reduction, neighbor graph construction, and clustering.

Scanpy (Python)

Goal: Reduce dimensions, build neighbor graphs, cluster cells, and visualize with UMAP/tSNE using Scanpy.

Approach: Run PCA for dimensionality reduction, construct a k-NN graph, apply Leiden community detection, and compute UMAP embedding.

"Cluster cells and find groups" → Reduce dimensionality with PCA, build a neighborhood graph, partition cells into clusters, and embed in 2D for visualization.

Required Imports

import scanpy as sc
import matplotlib.pyplot as plt

PCA

# Run PCA
sc.tl.pca(adata, n_comps=50, svd_solver='arpack')

# Visualize variance explained
sc.pl.pca_variance_ratio(adata, n_pcs=50)

# Visualize PCA
sc.pl.pca(adata, color='n_genes_by_counts')

Determine Number of PCs

# Elbow plot to choose number of PCs
sc.pl.pca_variance_ratio(adata, n_pcs=50, log=True)

# Typically use 10-50 PCs based on elbow
n_pcs = 30

Compute Neighbors

# Build k-nearest neighbor graph
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)

Clustering (Leiden - Recommended)

# Leiden clustering (preferred over Louvain)
sc.tl.leiden(adata, resolution=0.5)

# Higher resolution = more clusters
sc.tl.leiden(adata, resolution=1.0, key_added='leiden_r1')

# View cluster sizes
adata.obs['leiden'].value_counts()

Clustering (Louvain)

# Louvain clustering (alternative)
sc.tl.louvain(adata, resolution=0.5)

UMAP

# Compute UMAP embedding
sc.tl.umap(adata, min_dist=0.3, spread=1.0)

# Visualize clusters on UMAP
sc.pl.umap(adata, color='leiden')

# Color by gene expression
sc.pl.umap(adata, color=['leiden', 'CD3D', 'MS4A1', 'CD14'])

tSNE

# Compute tSNE (slower than UMAP)
sc.tl.tsne(adata, n_pcs=30, perplexity=30)

# Visualize
sc.pl.tsne(adata, color='leiden')

Complete Clustering Pipeline

Goal: Run end-to-end clustering from preprocessed data to UMAP visualization.

Approach: Chain PCA, neighbor computation, Leiden clustering, and UMAP into a single pipeline.

import scanpy as sc

# Assumes preprocessed data
adata = sc.read_h5ad('preprocessed.h5ad')

# PCA
sc.tl.pca(adata, n_comps=50)

# Neighbors
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)

# Cluster
sc.tl.leiden(adata, resolution=0.5)

# UMAP
sc.tl.umap(adata)

# Visualize
sc.pl.umap(adata, color='leiden')

Exploring Different Resolutions

Goal: Evaluate clustering at multiple resolutions to find the appropriate granularity.

Approach: Iterate over resolution values, cluster at each, and compare cluster counts on UMAP.

# Try multiple resolutions
for res in [0.2, 0.5, 0.8, 1.0, 1.5]:
    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_r{res}')
    n_clusters = adata.obs[f'leiden_r{res}'].nunique()
    print(f'Resolution {res}: {n_clusters} clusters')

# Compare on UMAP
sc.pl.umap(adata, color=['leiden_r0.2', 'leiden_r0.5', 'leiden_r1.0'], ncols=3)

PAGA (Trajectory Inference)

# Partition-based graph abstraction
sc.tl.paga(adata, groups='leiden')
sc.pl.paga(adata, color='leiden')

# Use PAGA for UMAP initialization
sc.tl.umap(adata, init_pos='paga')

---

Seurat (R)

Goal: Reduce dimensions, build neighbor graphs, cluster cells, and visualize with UMAP/tSNE using Seurat.

Approach: Run PCA, determine optimal PC count, construct SNN graph, apply Louvain clustering, and compute UMAP embedding.

Required Libraries

library(Seurat)
library(ggplot2)

PCA

# Run PCA
seurat_obj <- RunPCA(seurat_obj, features = VariableFeatures(seurat_obj), npcs = 50)

# Visualize PCA
DimPlot(seurat_obj, reduction = 'pca')
VizDimLoadings(seurat_obj, dims = 1:2, reduction = 'pca')

# Heatmaps of PC genes
DimHeatmap(seurat_obj, dims = 1:6, cells = 500, balanced = TRUE)

Determine Number of PCs

# Elbow plot
ElbowPlot(seurat_obj, ndims = 50)

# JackStraw (more rigorous but slow)
seurat_obj <- JackStraw(seurat_obj, num.replicate = 100)
seurat_obj <- ScoreJackStraw(seurat_obj, dims = 1:20)
JackStrawPlot(seurat_obj, dims = 1:20)

Find Neighbors

# Build KNN graph
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)

Find Clusters

# Louvain clustering (default)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)

# View cluster assignments
head(Idents(seurat_obj))
table(Idents(seurat_obj))

Exploring Different Resolutions

# Try multiple resolutions
seurat_obj <- FindClusters(seurat_obj, resolution = c(0.2, 0.5, 0.8, 1.0, 1.5))

# Results stored in metadata
head([e-mail])

# Compare resolutions
library(clustree)
clustree(seurat_obj, prefix = 'RNA_snn_res.')

UMAP

# Run UMAP
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

# Visualize
DimPlot(seurat_obj, reduction = 'umap', label = TRUE)

# Split by sample
DimPlot(seurat_obj, reduction = 'umap', split.by = 'sample')

tSNE

# Run tSNE
seurat_obj <- RunTSNE(seurat_obj, dims = 1:30)

# Visualize
DimPlot(seurat_obj, reduction = 'tsne')

Complete Clustering Pipeline

Goal: Run end-to-end Seurat clustering from preprocessed data to UMAP visualization.

Approach: Chain PCA, neighbor finding, cluster detection, and UMAP into a single pipeline.

library(Seurat)

# Assumes preprocessed data
seurat_obj <- readRDS('preprocessed.rds')

# PCA
seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE)

# Neighbors
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)

# Cluster
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)

# UMAP
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

# Visualize
DimPlot(seurat_obj, reduction = 'umap', label = TRUE)

Access Embeddings

# Get PCA coordinates
pca_coords <- Embeddings(seurat_obj, reduction = 'pca')

# Get UMAP coordinates
umap_coords <- Embeddings(seurat_obj, reduction = 'umap')

# Add to metadata for custom plotting
seurat_obj$UMAP_1 <- umap_coords[, 1]
seurat_obj$UMAP_2 <- umap_coords[, 2]

---

Parameter Reference

Parameter	Typical Values	Effect
n_pcs	10-50	More PCs capture more variance
n_neighbors	10-30	Higher = smoother, lower = more local
resolution	0.2-2.0	Higher = more clusters
min_dist (UMAP)	0.1-0.5	Lower = tighter clusters

Method Comparison

Step	Scanpy	Seurat
PCA	sc.tl.pca()	RunPCA()
Neighbors	sc.pp.neighbors()	FindNeighbors()
Cluster	sc.tl.leiden()	FindClusters()
UMAP	sc.tl.umap()	RunUMAP()
tSNE	sc.tl.tsne()	RunTSNE()

Related Skills

• preprocessing - Data must be preprocessed before clustering
• markers-annotation - Find markers for each cluster
• data-io - Save clustered results