Bio Single Cell Clustering

Version Compatibility

Reference examples tested with: scanpy 1.10+, Seurat 5.0+, anndata 0.10+

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

"Cluster my cells" -> Build a k-nearest-neighbor graph in PCA space, partition it into communities, and embed in 2D for display.

• Python: sc.pp.neighbors + sc.tl.leiden + sc.tl.umap (Scanpy)
• R: FindNeighbors + FindClusters + RunUMAP (Seurat)

Governing Principle

Resolution is not a truth knob, and clusters are not discoveries. Graph-based clustering partitions a kNN graph built in PCA space; there is no ground truth, no "correct" number of clusters, and resolution selects a scale of description rather than revealing one. Clusters are hypotheses that must be validated by markers, stability, and (where claims are made) a significance test before any partition is named a cell population. Over-clustering is the default failure mode: any homogeneous blob can be bisected, and a higher resolution will always split it further. UMAP/tSNE distances, cluster sizes, and apparent gaps are artifacts of a non-linear neighbor-preserving objective and are not metric data (Chari & Pachter 2023) - cluster on the graph, never on embedding coordinates. The deepest trap: clustering chooses labels to maximize between-group separation, so running a marker test on those same clusters tests a hypothesis built from the data used to test it (double-dipping), and the resulting p-values are not merely inflated, they are invalid inference.

Leiden vs Louvain

Leiden is the current default for graph community detection because Louvain can return internally disconnected communities (Traag 2019). Seurat still ships Louvain as its default algorithm; Scanpy uses Leiden but is mid-migration between backends. Methodology evolves - verify the current default and backend against the installed package docs before pinning a pipeline.

Scanpy 1.10 backend migration (pin for reproducibility): sc.tl.leiden still defaults to the leidenalg backend through 1.10-1.12 and emits a FutureWarning that the default will switch to igraph. The exact flip version is unconfirmed, so pin the backend explicitly: sc.tl.leiden(adata, flavor='igraph', n_iterations=2, directed=False). Switching backend or n_iterations changes the labels - a pipeline that pins neither is non-reproducible across versions. Seurat's Leiden (algorithm=4) requires the leidenalg Python module via reticulate, which is why most Seurat pipelines still run Louvain.

Parameter Reference

n_pcs dominates the result far more than n_neighbors and is the largest under-tuned lever - too few collapses real structure, too many reintroduces technical noise.

Resolution is an unidentifiable nuisance parameter: it cannot be validated internally (no ground truth), so tuning it until clusters "match known cell types" is confirmation bias laundered as analysis. Sweep a range, visualize cell flow with clustree, and pick the coarsest level whose populations are defensible by orthogonal evidence - label finer splits as hypotheses.

Cluster Cells with Scanpy

Goal: Reduce dimensions, build the neighbor graph, partition with Leiden, and embed for display. Approach: PCA -> kNN graph on a chosen n_pcs -> Leiden with a pinned backend -> UMAP.

import scanpy as sc

adata = sc.read_h5ad('preprocessed.h5ad')

sc.tl.pca(adata, n_comps=50, svd_solver='arpack')
sc.pl.pca_variance_ratio(adata, n_pcs=50, log=True)  # elbow to choose n_pcs

sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5, flavor='igraph', n_iterations=2, directed=False)
adata.obs['leiden'].value_counts()

sc.tl.umap(adata, min_dist=0.3)
sc.pl.umap(adata, color=['leiden', 'CD3D', 'MS4A1', 'CD14'])

Sweep and Validate Resolution

Goal: Choose a defensible granularity instead of a single tuned-to-taste resolution. Approach: Cluster across a resolution range, inspect cell flow (clustree), and confirm each cluster carries distinct markers.

import scanpy as sc

for res in [0.2, 0.4, 0.6, 0.8, 1.0]:
    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_r{res}',
                 flavor='igraph', n_iterations=2, directed=False)
    print(res, adata.obs[f'leiden_r{res}'].nunique(), 'clusters')

sc.pl.umap(adata, color=['leiden_r0.2', 'leiden_r0.6', 'leiden_r1.0'], ncols=3)
# clustree (R) or sc.tl.dendrogram for flow across resolutions; merge clusters
# whose top markers are indistinguishable -> they are one population over-split

Cluster Cells with Seurat

Goal: Run PCA, build the SNN graph, partition, and embed in Seurat. Approach: RunPCA -> FindNeighbors on chosen dims -> FindClusters (sweep resolutions) -> RunUMAP.

library(Seurat)

seurat_obj <- readRDS('preprocessed.rds')
seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE)
ElbowPlot(seurat_obj, ndims = 50)  # choose dims

seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj, resolution = c(0.2, 0.4, 0.6, 0.8, 1.0))
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

library(clustree)
clustree(seurat_obj, prefix = 'RNA_snn_res.')  # cell flow across the sweep
DimPlot(seurat_obj, reduction = 'umap', label = TRUE)

FindClusters defaults to Louvain (algorithm=1); pass algorithm=4 for Leiden (requires the leidenalg Python module). Resolutions stored as RNA_snn_res.<r> columns feed clustree directly.

Subclustering

Goal: Resolve fine states inside a coarse cluster without importing global axes. Approach: Subset the cluster, then recompute HVGs, PCA, and the kNN graph on the subset.

sub = adata[adata.obs['leiden'] == '3'].copy()
sc.pp.highly_variable_genes(sub, n_top_genes=2000)
sub = sub[:, sub.var.highly_variable]
sc.pp.scale(sub, max_value=10)
sc.tl.pca(sub, n_comps=30)
sc.pp.neighbors(sub, n_neighbors=15, n_pcs=20)
sc.tl.leiden(sub, resolution=0.4, flavor='igraph', n_iterations=2, directed=False)

Reusing the global PCA imports axes uninformative within a homogeneous subset and manufactures artifactual sub-splits. Subclustering compounds double-dipping (cells selected twice), so stop when splits lose distinct markers or fail a significance test - not when resolution can technically still split (it always can).

Validating That Clusters Are Real

Stability and significance are separate questions, and both differ from biological reality.

• Stability (necessary, not sufficient): bootstrap cells, re-cluster, measure per-cluster label agreement (Jaccard >= ~0.6-0.7 = stable). A perfectly reproducible split can still be technical - driven by cell-cycle phase, dissociation stress (FOS/JUN/HSPA1A), mitochondrial fraction, ambient RNA, or batch. Stable does not mean real.
• Significance (whether a split is two populations or one): scSHC (Grabski 2023) and CHOIR (2025) test each split under a null with error control; a failed split is over-clustering and the clusters should be merged.
• Biological-vs-technical adjudication: check that a split survives regressing out cycle/mito and carries non-stress markers before claiming a population.

Double-dipping (post-clustering inference is invalid, not just inflated): rank_genes_groups/FindAllMarkers p-values are conditioned on a clustering chosen to maximize separation, so under one homogeneous population they do not follow their nominal null - type-I error approaches 1 as resolution rises, and BH correction does nothing because the p-values are invalid before correction. Use these marker tests for ranking and labeling only. To make a defensible claim that a cluster is a distinct population, pair a cluster significance test (scSHC/CHOIR) with a double-dipping-robust DE method (ClusterDE's synthetic null, or count splitting where the noise model holds - Poisson thinning on overdispersed counts silently reinstates the bias). See markers-annotation for marker testing and the pseudobulk path for cross-condition DE.

UMAP and tSNE Are Visualization Only

Cluster on the graph or PCA, never on embedding coordinates. Inter-cluster distances, relative sizes, and apparent gaps in UMAP/tSNE are artifacts of the embedding objective and are not metric (Chari & Pachter 2023) - do not read them as lineage, evolutionary distance, or population separation. "Cells far apart in UMAP are more different" and "UMAP preserves global structure" are folklore; PCA initialization plus high perplexity makes tSNE less misleading but does not make distances trustworthy (Kobak & Berens 2019). Embeddings display graph-derived labels; back every structural claim with the graph, validly-tested markers, or quantitative analysis in PCA space.

Common Errors

Related Skills

• preprocessing - QC, normalization, and HVG selection that must precede clustering
• doublet-detection - Remove doublets before clustering so they do not form fake intermediate clusters
• batch-integration - Integrate batches before clustering when a cluster tracks a single sample
• markers-annotation - Find and test markers per cluster (with the double-dipping caveat)
• cell-annotation - Assign cell-type identities to validated clusters
• single-cell/differential-abundance - Test whether cluster proportions shift across conditions
• data-visualization/dimensionality-reduction-plots - Publication-quality UMAP/tSNE/PCA figures
• pathway-analysis/go-enrichment - Interpret per-cluster marker sets

References

• Traag, Waltman & van Eck (2019). From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9:5233.
• Chari & Pachter (2023). The specious art of single-cell genomics. PLoS Comput Biol 19(8):e1011288.
• Kobak & Berens (2019). The art of using t-SNE for single-cell transcriptomics. Nat Commun 10:5416.
• Grabski, Street & Irizarry (2023). Significance analysis for clustering with single-cell RNA-sequencing data. Nat Methods 20:1196-1202.
• Neufeld, Gao, Popp, Battle & Witten (2024). Inference after latent variable estimation for single-cell RNA-seq (count splitting). Biostatistics 25(1):270-287.
• Zappia & Oshlack (2018). Clustering trees: a visualization for evaluating clusterings at multiple resolutions. GigaScience 7(7):giy083.