seuratclustering

name	seuratclustering
description	Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters. Clusters can be explored at multiple resolutions to balance granularity and biological relevance.

SeuratClustering Process Configuration

Purpose

Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters. Clusters can be explored at multiple resolutions to balance granularity and biological relevance.

When to Use

• After SeuratPreparing: Standard workflow after QC and normalization
• T/B cell selection: After SeuratClusteringOfAllCells (if TOrBCellSelection enabled)
• Reference-based annotation: Alternative to SeuratMap2Ref or CellTypeAnnotation
• Standard clustering: When you need unsupervised cell type discovery
• Multi-resolution exploration: When unsure of optimal cluster granularity

Configuration Structure

Process Enablement

[SeuratClustering]
cache = true  # Cache intermediate results for faster re-runs

Input Specification

[SeuratClustering.in]
srtobj = ["SeuratPreparing"]  # Path or reference to Seurat object

Environment Variables

Core Parameters

[SeuratClustering.envs]
# Number of cores for parallelization
ncores = 1  # int; Higher values speed up computation

# Metadata column name for cluster labels
ident = "seurat_clusters"  # Default column name

# Cache location for intermediate results
cache = "/tmp"  # Path; Set to false to disable caching

FindNeighbors Parameters

[SeuratClustering.envs.FindNeighbors]
# K-nearest neighbors: Defines neighborhood size (default: 20)
# Larger values capture more global structure
k.param = 20  # int; Range: 5-100 depending on dataset size

# Reduction to use for building neighbor graph
# If not specified, uses sobj@misc$integrated_new_reduction
reduction = "pca"  # Options: "pca", "integrated.cca", "integrated.rpca", etc.

# Dimensions to use from reduction
dims = 30  # int or 1:N; Automatically expanded to 1:dims

# Pruning threshold for shared nearest neighbor (SNN) graph
# 0 = no pruning, 1 = prune everything
prune.SNN = 0.067  # Default: 1/15; Controls graph connectivity

# Nearest neighbor method
nn.method = "annoy"  # Options: "annoy", "rann"

# Graph naming (for multiple integration methods)
graph.name = ["pca_nn", "pca_snn"]  # [NN, SNN] graph names

Full FindNeighbors Parameter List:

• k.param (int): Number of nearest neighbors (default: 20)
• reduction (str): Dimensional reduction to use (default: "pca")
• dims (int): Number of dimensions to use (default: 1:10)
• assay (str): Assay to use when dims is NULL
• features (list): Features to use when dims is NULL
• compute.SNN (bool): Compute shared nearest neighbor graph (default: TRUE)
• prune.SNN (float): SNN pruning threshold (default: 1/15)
• nn.method (str): NN algorithm - "annoy" or "rann" (default: "annoy")
• n.trees (int): Annoy tree count (default: 50)
• annoy.metric (str): Distance metric - "euclidean", "cosine", "manhattan", "hamming" (default: "euclidean")
• graph.name (list): Names for NN and SNN graphs
• verbose (bool): Print output (default: TRUE)

RunUMAP Parameters

[SeuratClustering.envs.RunUMAP]
# Reduction to use for UMAP embedding
reduction = "pca"  # Options: "pca", "integrated.cca", "integrated.rpca"

# Dimensions to use
dims = 30  # int; Automatically expanded to 1:dims

# Use specific features instead of dimensions
# Can be a list with "order" and "n" fields
features = 30  # or list: {order = "desc(abs(avg_log2FC))", n = 30}

# Number of neighboring points (global vs local structure)
n.neighbors = 30  # int; Range: 5-50; Higher = more global

# Min distance controls cluster tightness
min.dist = 0.3  # float; Range: 0.001-0.5; Higher = more spread out

# Effective scale of embedded points
spread = 1  # float; Works with min.dist for cluster distribution

# Number of embedding dimensions
n.components = 2  # int; Usually 2 for visualization

# Distance metric
metric = "cosine"  # Options: "euclidean", "cosine", "manhattan", etc.

# Learning rate for optimization
learning.rate = 1  # float; Initial learning rate

# Random seed for reproducibility
seed.use = 42  # int

Full RunUMAP Parameter List:

• dims (int): Dimensions to use (default: NULL, uses reduction)
• reduction (str): Reduction to use (default: "pca")
• features (list/int): Features to use instead of dims
• n.neighbors (int): Neighborhood size (default: 30)
• n.components (int): Embedding dimensions (default: 2)
• metric (str): Distance metric (default: "cosine")
• min.dist (float): Cluster tightness (default: 0.3)
• spread (float): Scale of embedding (default: 1.0)
• learning.rate (float): Initial learning rate (default: 1.0)
• n.epochs (int): Training epochs (default: auto: 200 large, 500 small)
• set.op.mix.ratio (float): Fuzzy set operation ratio (default: 1.0)
• local.connectivity (int): Local connectivity (default: 1)
• seed.use (int): Random seed (default: 42)
• reduction.name (str): Name for UMAP reduction (default: "umap")
• verbose (bool): Print output (default: TRUE)

FindClusters Parameters

[SeuratClustering.envs.FindClusters]
# Resolution: Higher = more clusters, Lower = fewer clusters
resolution = 0.8  # float; Default: 0.8
# Multiple resolutions supported: [0.4, 0.6, 0.8, 1.0]
# Range syntax: "0.2:1.0:0.1" -> [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

# Clustering algorithm (1=Louvain, 2=Louvain multilevel, 3=SLM, 4=Leiden)
# Leiden (4) is preferred over Louvain (1-3)
algorithm = 4  # int; 1-4; 4 = Leiden (recommended)

# Leiden implementation
leiden_method = "leidenbase"  # Options: "leidenbase", "igraph"

# Leiden objective function
leiden_objective_function = "modularity"  # Options: "modularity", "CPM"

# Random seed for reproducibility
random.seed = 0  # int

# Number of random starts
n.start = 10  # int

# Max iterations per start
n.iter = 10  # int

# Graph to use for clustering
graph.name = "pca_snn"  # Must match FindNeighbors graph.name[1]

# Cluster name in metadata
cluster.name = "seurat_clusters"  # Can use envs.ident instead

# Group singletons into nearest cluster
group.singletons = TRUE  # bool

Full FindClusters Parameter List:

• resolution (float): Cluster granularity (default: 0.8)
• algorithm (int): 1=Louvain, 2=Louvain multilevel, 3=SLM, 4=Leiden (default: 1)
• leiden_method (str): "leidenbase" or "igraph" (default: "leidenbase")
• leiden_objective_function (str): "modularity" or "CPM" (default: "modularity")
• random.seed (int): Random seed (default: 0)
• n.start (int): Random starts (default: 10)
• n.iter (int): Max iterations (default: 10)
• graph.name (str): SNN graph name to use
• cluster.name (str): Metadata column for clusters
• modularity.fxn (int): Modularity function (default: 1)
• group.singletons (bool): Group singletons (default: TRUE)
• verbose (bool): Print output (default: TRUE)

External References

FindNeighbors (Seurat v5)

https://satijalab.org/seurat/reference/findneighbors

• Constructs shared nearest neighbor (SNN) graph
• Computes k-nearest neighbors and Jaccard index for neighborhood overlap
• Pruning controls graph connectivity (higher = stricter)

RunUMAP (Seurat v5)

https://satijalab.org/seurat/reference/runumap

• Uniform Manifold Approximation and Projection for visualization
• n.neighbors: Global structure vs local detail trade-off (5-50)
• min.dist: Cluster tightness (0.001-0.5)
• spread: Scale of embedding (works with min.dist)

FindClusters (Seurat v5)

https://www.rdocumentation.org/packages/Seurat/versions/5.3.1/topics/FindClusters

• Louvain (1-3) vs Leiden (4) clustering algorithms
• Leiden preferred: Better community detection, improved over Louvain
• Resolution: >1.0 = more clusters, <1.0 = fewer clusters

Algorithm Comparison: Leiden vs Louvain

• Leiden (algorithm=4): Preferred, better community detection, refined clusters
• Louvain (algorithm=1-3): Faster but less accurate, can produce badly connected communities
• Source: Single-cell best practices recommend Leiden https://www.sc-best-practices.org/cellular_structure/clustering.html

Integration Method Support

When using SeuratData integration workflows, use integrated reduction:

• CCA integration: reduction = "integrated.cca"
• RPCA integration: reduction = "integrated.rpca"
• Harmony integration: reduction = "integrated.harmony"

Configuration Examples

Minimal Configuration

[SeuratClustering]
[SeuratClustering.in]
srtobj = ["SeuratPreparing"]

Result: Uses defaults (PCA, 30 dims, resolution 0.8, Louvain algorithm)

Standard Resolution Sweep

[SeuratClustering]
[SeuratClustering.in]
srtobj = ["SeuratPreparing"]

[SeuratClustering.envs.FindClusters]
resolution = [0.4, 0.6, 0.8, 1.0]  # Test 4 resolutions

Result: Creates seurat_clusters_0.4, seurat_clusters_0.6, etc. Final = 1.0

Range Syntax for Resolution Sweep

[SeuratClustering.envs.FindClusters]
# From 0.2 to 1.0 with step 0.1
resolution = "0.2:1.0:0.1"  # Equivalent to [0.2, 0.3, ..., 1.0]

Leiden Algorithm with Custom Parameters

[SeuratClustering]

[SeuratClustering.envs.FindNeighbors]
k.param = 30  # Larger neighborhood
prune.SNN = 0.05  # Less pruning
graph.name = ["pca_nn", "pca_snn"]

[SeuratClustering.envs.FindClusters]
algorithm = 4  # Leiden
resolution = 1.2  # Higher resolution for more clusters
random.seed = 42  # Reproducible clustering
graph.name = "pca_snn"

Integrated Data (CCA/RPCA)

[SeuratClustering]

[SeuratClustering.envs.FindNeighbors]
reduction = "integrated.cca"  # Use integrated reduction
dims = 30

[SeuratClustering.envs.RunUMAP]
reduction = "integrated.cca"
dims = 30
reduction.name = "umap.cca"

[SeuratClustering.envs.FindClusters]
resolution = 1.0

Custom UMAP Parameters for Better Separation

[SeuratClustering]

[SeuratClustering.envs.RunUMAP]
n.neighbors = 15  # More local detail
min.dist = 0.1  # Tighter clusters
spread = 1.5  # More spread out
seed.use = 123

Using Top Markers for UMAP

[SeuratClustering]

[SeuratClustering.envs.RunUMAP]
# Use top 30 markers for UMAP instead of PCs
features = {order = "desc(abs(avg_log2FC))", n = 30}

Multi-Process with Custom Cluster Names

[SeuratClustering]
[SeuratClustering.envs]
ident = "my_clusters"  # Custom column name

[CellTypeAnnotation]
[CellTypeAnnotation.envs]
newcol = "cell_types"  # Different column to avoid overwriting

[SeuratMap2Ref]
[SeuratMap2Ref.envs]
name = "ref_clusters"  # Another clustering method

Common Patterns

Pattern 1: Single Resolution (Standard)

[SeuratClustering]

[SeuratClustering.envs.FindClusters]
resolution = 0.8  # Default balanced resolution

Pattern 2: Resolution Sweep for Exploration

[SeuratClustering]

[SeuratClustering.envs.FindClusters]
resolution = "0.4:1.2:0.2"  # [0.4, 0.6, 0.8, 1.0, 1.2]

Pattern 3: Leiden with High Resolution (Fine-grained)

[SeuratClustering]

[SeuratClustering.envs.FindNeighbors]
k.param = 25

[SeuratClustering.envs.FindClusters]
algorithm = 4  # Leiden
resolution = 1.5  # More clusters

Pattern 4: Integrated Data (Post-Integration)

[SeuratClustering]

[SeuratClustering.envs.FindNeighbors]
reduction = "integrated.cca"
dims = 30

[SeuratClustering.envs.RunUMAP]
reduction = "integrated.cca"
dims = 30

[SeuratClustering.envs.FindClusters]
resolution = 1.0

Pattern 5: Sparse UMAP for Large Datasets

[SeuratClustering]

[SeuratClustering.envs.FindNeighbors]
nn.method = "annoy"
n.trees = 50

[SeuratClustering.envs.RunUMAP]
n.neighbors = 50  # More global for large datasets

Dependencies

Upstream Processes

• Required: SeuratPreparing (or SeuratClusteringOfAllCells if TOrBCellSelection used)
• Optional: LoadingRNAFromSeurat with prepared = false (if loading unprepared Seurat object)

Downstream Processes

• SeuratClusterStats: Cluster statistics and quality metrics
• ClusterMarkers: Differential expression between clusters
• MarkersFinder: Flexible marker finding with enrichment analysis
• ScRepCombiningExpression: If TCR data present (combines RNA + TCR)
• TESSA: TCR-specific clustering analysis

Validation Rules

Resolution Constraints

• Must be positive (resolution > 0)
• Single value or list of values allowed
• Range syntax: "start:end:step" (step defaults to 0.1 if omitted)

Dimension Requirements

• dims must not exceed available dimensions in reduction
• Automatically truncated to min(dims, ncol(reduction) - 1)

Graph Name Consistency

• FindClusters.graph.name must match FindNeighbors.graph.name[1] (SNN graph name)
• When using multiple integration methods, use unique graph names

Algorithm Selection

• Louvain: algorithm = 1 (original), 2 (multilevel), 3 (SLM)
• Leiden: algorithm = 4 (recommended)
• Leiden requires leiden_method and leiden_objective_function parameters

Troubleshooting

Issue: Too Many Small Clusters

Symptoms: Hundreds of tiny clusters, many singletons

Solutions:

[SeuratClustering.envs.FindClusters]
resolution = 0.4  # Lower resolution
algorithm = 4  # Leiden handles singletons better
group.singletons = TRUE  # Group singletons into nearest cluster

Issue: Clusters Overlapping in UMAP

Symptoms: Poor separation in UMAP visualization

Solutions:

[SeuratClustering.envs.RunUMAP]
min.dist = 0.1  # Tighter clusters
n.neighbors = 15  # More local detail
spread = 1.2  # More separation

Issue: Clustering Not Reproducible

Symptoms: Different clusters on each run

Solutions:

[SeuratClustering.envs.FindNeighbors]
# Set seed for reproducible nearest neighbors
seed.use = 42

[SeuratClustering.envs.FindClusters]
random.seed = 42
n.start = 10  # More starts for stable results

Issue: Slow Performance

Symptoms: Clustering takes hours

Solutions:

[SeuratClustering.envs]
ncores = 8  # Use more cores

[SeuratClustering.envs.FindNeighbors]
nn.method = "annoy"  # Faster approximate NN
dims = 20  # Fewer dimensions

[SeuratClustering.envs.RunUMAP]
n.epochs = 200  # Fewer epochs (default auto-selects based on size)

Issue: Badly Connected Communities (Louvain)

Symptoms: Leiden warning about disconnected clusters

Solutions:

[SeuratClustering.envs.FindClusters]
algorithm = 4  # Switch to Leiden
leiden_method = "leidenbase"  # Use leidenbase implementation

Issue: Graph Name Conflicts with Multiple Integrations

Symptoms: Wrong graph used for clustering

Solutions:

# CCA integration
[SeuratClustering.envs.FindNeighbors]
reduction = "integrated.cca"
graph.name = ["cca_nn", "cca_snn"]

[SeuratClustering.envs.FindClusters]
graph.name = "cca_snn"  # Must match SNN graph name

# RPCA integration
[SeuratClustering.envs.FindNeighbors]
reduction = "integrated.rpca"
graph.name = ["rpca_nn", "rpca_snn"]

[SeuratClustering.envs.FindClusters]
graph.name = "rpca_snn"

Issue: Clustering Not Using Integration

Symptoms: Clustering on raw RNA instead of integrated data

Solutions:

[SeuratClustering.envs.FindNeighbors]
reduction = "integrated.cca"  # Specify integrated reduction
dims = 30

# If using SCTransform + integration:
[SeuratPreparing.envs]
integration_method = "CCA"  # Ensure integration is performed

Best Practices

1. Use Leiden algorithm (algorithm = 4) for better community detection
2. Test multiple resolutions to find optimal granularity
3. Set random seeds for reproducible results
4. Match reduction to integration if using CCA/RPCA/Harmony
5. Custom cluster names when running multiple annotation methods to avoid overwriting
6. Cache intermediate results for faster re-runs with different parameters
7. Parallelize with ncores for large datasets (>50k cells)
8. Use resolution sweeps when unsure of optimal granularity

Related Processes

• SeuratClusteringOfAllCells: Clustering before T/B cell selection
• SeuratSubClustering: Re-clustering within specific clusters
• SeuratMap2Ref: Reference-based supervised clustering
• CellTypeAnnotation: Automated cell type annotation