SeuratSubClustering Process Configuration

Purpose

Performs fine-grained re-clustering on specific subsets of cells (e.g., individual clusters, cell types, or custom subsets). Unlike Seurat::FindSubCluster which only finds subclusters within a single cluster, this process performs the complete clustering workflow (PCA, UMAP, FindNeighbors, FindClusters) on any subset of cells defined by metadata filters or cell barcode lists.

When to Use

• Cluster heterogeneity analysis: When initial clustering identifies mixed cell populations within a cluster
• Cell type sub-clustering: To resolve heterogeneity within annotated cell types (e.g., T cell subsets: CD4+, CD8+, naive, memory, effector)
• Lineage-specific analysis: To examine substructure within major cell lineages
• Differential sub-populations: When a cluster contains multiple biologically distinct populations (e.g., NK cells + CD4 T cells)
• Multi-resolution exploration: To test different clustering granularities on specific cell subsets
• Downstream marker discovery: When you need markers for sub-populations within larger clusters

Configuration Structure

Process Enablement

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

Input Specification

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

Environment Variables

Core Parameters

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

# Metadata mutaters to define subset cells
# Applied BEFORE subsetting to create temporary columns
mutaters = {}  # json; Dictionary of dplyr-like mutations

# Expression to subset cells (dplyr::filter syntax)
# Applied to metadata using tidyseurat::filter()
subset = "seurat_clusters == 'c3'"  # str; Filter expression

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

Sub-clustering Cases (Multiple Subsets)

[SeuratSubClustering.envs.cases]
# Keys are case names (prefixes for outputs)
# Values inherit envs parameters (except mutaters, cache)
# If empty, default case "subcluster" is created

Case Naming Rules:

• Case name becomes prefix for reductions: <CASENAME>PC_, <CASENAME>UMAP_
• Case name becomes prefix for cluster columns: <CASENAME>.<resolution>
• Case name becomes final cluster column: <CASENAME>
• Non-alphanumeric characters in case names are removed

Metadata Output:

• Each case adds new metadata columns to original Seurat object
• Reductions saved: <CASENAME>.pc, <CASENAME>.umap
• Clusters saved: <CASENAME>.<resolution> for each resolution
• Final clusters: <CASENAME> column

RunPCA Parameters

[SeuratSubClustering.envs.RunPCA]
# See https://satijalab.org/seurat/reference/runpca
# object specified internally as subset object
npcs = 30  # int; Number of PCs to compute

RunUMAP Parameters

[SeuratSubClustering.envs.RunUMAP]
# See https://satijalab.org/seurat/reference/runumap
# object specified internally as subset object
# dims=N expanded to dims=1:N (min(N, ncol-1))

dims = 30  # int; Number of PCs to use

# Use specific features instead of dimensions
# Can be list: {order = "desc(abs(avg_log2FC))", n = 30}
# Or numeric (treated as n with default order)
features = 30  # int or list; Top markers for UMAP

# Reduction to use for UMAP
reduction = "pca"  # str; Uses sobj@misc$integrated_new_reduction if omitted

n.neighbors = 30  # int; Neighborhood size
min.dist = 0.3  # float; Cluster tightness (0.001-0.5)
spread = 1  # float; Embedding scale
seed.use = 42  # int; Random seed

FindNeighbors Parameters

[SeuratSubClustering.envs.FindNeighbors]
# See https://satijalab.org/seurat/reference/findneighbors
# object specified internally

reduction = "pca"  # str; Uses sobj@misc$integrated_new_reduction if omitted
dims = 30  # int; Dimensions to use
k.param = 20  # int; K-nearest neighbors
prune.SNN = 0.067  # float; SNN pruning threshold (default: 1/15)
nn.method = "annoy"  # str; "annoy" or "rann"

FindClusters Parameters

[SeuratSubClustering.envs.FindClusters]
# See https://satijalab.org/seurat/reference/findclusters
# object specified internally

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

# Cluster labels prefixed with "s" (s1, s2, ...) instead of (s0, s1, ...)
algorithm = 1  # int; 1=Louvain, 4=Leiden (recommended)
graph.name = "pca_snn"  # str; Must match FindNeighbors SNN graph
random.seed = 0  # int; Reproducibility

Multi-resolution Output:

• Multiple resolutions create columns: <CASENAME>_0.4, <CASENAME>_0.6, <CASENAME>_0.8, <CASENAME>
• Final resolution uses last value in list

External References

Seurat Functions

• RunPCA(): https://satijalab.org/seurat/reference/runpca
  • Principal component analysis on subset of cells
• RunUMAP(): https://satijalab.org/seurat/reference/runumap
  • Non-linear dimensionality reduction for visualization
• FindNeighbors(): https://satijalab.org/seurat/reference/findneighbors
  • K-nearest neighbor graph construction
• FindClusters(): https://satijalab.org/seurat/reference/findclusters
  • Community detection (Louvain/Leiden algorithms)

tidyseurat::filter()

https://stemangiola.github.io/tidyseurat/reference/filter.html

• Subset Seurat objects using dplyr-like filter syntax
• Supports logical expressions: seurat_clusters == 'c3', celltype %in% c('CD4', 'CD8')
• Can use any metadata column created by mutaters

Configuration Examples

Minimal Configuration (Default Case)

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

Result: Creates default case "subcluster" with all cells

Single Cluster Sub-clustering

[SeuratSubClustering]
[SeuratSubClustering.envs]
subset = "seurat_clusters == 'c3'"

Result: Re-clusters only cells in cluster c3

Metadata-Based Sub-clustering (Cell Type)

[SeuratSubClustering]
[SeuratSubClustering.envs]
# First add cell type annotation via mutaters
mutaters = {is_cd4 = "if_else(celltype == 'CD4 T cell', TRUE, FALSE)"}

[SeuratSubClustering.envs.RunPCA]
npcs = 50

[SeuratSubClustering.envs.FindClusters]
resolution = 1.2
algorithm = 4  # Leiden

Result: Creates subcluster case for CD4+ cells only

Multiple Sub-clustering Cases

[SeuratSubClustering]
[SeuratSubClustering.envs]
# Define multiple sub-clustering cases
[SeuratSubClustering.envs.cases.TEffector]
subset = "celltype == 'CD8 T cell' & state == 'Effector'"
resolution = 1.0

[SeuratSubClustering.envs.cases.TNaive]
subset = "celltype == 'CD8 T cell' & state == 'Naive'"
resolution = 0.8

[SeuratSubClustering.envs.cases.CD4Memory]
subset = "celltype == 'CD4 T cell' & state == 'Memory'"
resolution = 1.5

Result: Three sub-clustering analyses with different resolutions

• Metadata columns: TEffector, TNaive, CD4Memory
• Reductions: TEFFECTORPC_, TNAIVEPC_, CD4MEMORYPC_, etc.
• Clusters: TEffector, TNaive, CD4Memory

Multi-resolution Sub-clustering

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.Cluster3]
subset = "seurat_clusters == 'c3'"

[SeuratSubClustering.envs.cases.Cluster3.FindClusters]
# Test multiple resolutions
resolution = "0.4:1.2:0.2"  # [0.4, 0.6, 0.8, 1.0, 1.2]
algorithm = 4  # Leiden

Result: Cluster3 has columns Cluster3_0.4, Cluster3_0.6, Cluster3_0.8, Cluster3_1.0, Cluster3

Using Top Markers for UMAP

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.MixedCluster]
subset = "seurat_clusters == 'c5'"

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

Result: Sub-cluster based on top DEGs for better separation

Leiden Algorithm with Custom Parameters

[SeuratSubClustering]
[SeuratSubClustering.envs]
ncores = 4

[SeuratSubClustering.envs.FindNeighbors]
k.param = 30
prune.SNN = 0.05

[SeuratSubClustering.envs.FindClusters]
algorithm = 4  # Leiden
resolution = 1.0
random.seed = 42

Complex Subset with Multiple Conditions

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.ActivatedT]
subset = "celltype %in% c('CD4 T cell', 'CD8 T cell') & activation == 'Activated'"

[SeuratSubClustering.envs.cases.ActivatedT.RunPCA]
npcs = 40

[SeuratSubClustering.envs.cases.ActivatedT.RunUMAP]
dims = 40
n.neighbors = 20
min.dist = 0.2

Common Patterns

Pattern 1: Single Cluster Deep Dive

[SeuratSubClustering]
[SeuratSubClustering.envs]
# Re-cluster cluster 3 to resolve heterogeneity
subset = "seurat_clusters == 'c3'"

Pattern 2: Multiple Lineage Sub-clustering

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.TCD4]
subset = "celltype == 'CD4 T cell'"

[SeuratSubClustering.envs.cases.TCD8]
subset = "celltype == 'CD8 T cell'"

[SeuratSubClustering.envs.cases.TGD]
subset = "celltype == 'Gamma delta T cell'"

[SeuratSubClustering.envs.cases.NK]
subset = "celltype == 'NK cell'"

Pattern 3: Functional State Sub-clustering

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.Effector]
subset = "state == 'Effector'"

[SeuratSubClustering.envs.cases.Effector.FindClusters]
resolution = 1.5  # Higher resolution for more sub-states

[SeuratSubClustering.envs.cases.Memory]
subset = "state == 'Memory'"

[SeuratSubClustering.envs.cases.Naive]
subset = "state == 'Naive'"

Pattern 4: Re-clustering Based on Clonality (TCR+)

[SeuratSubClustering]
[SeuratSubClustering.envs]
# After ScRepCombiningExpression adds clonality metadata
[SeuratSubClustering.envs.cases.ExpandedClones]
subset = "clone_size >= 5"  # Large clones

[SeuratSubClustering.envs.cases.ExpandedClones.FindClusters]
resolution = 0.6  # Lower resolution for broader groups

[SeuratSubClustering.envs.cases.RareClones]
subset = "clone_size == 1"  # Unique clones

[SeuratSubClustering.envs.cases.RareClones.FindClusters]
resolution = 1.2  # Higher resolution to capture diversity

Pattern 5: Multi-resolution Exploration

[SeuratSubClustering]
[SeuratSubClustering.envs.cases.TumorCluster]
subset = "seurat_clusters == 'c8'"

[SeuratSubClustering.envs.cases.TumorCluster.FindClusters]
resolution = "0.2:2.0:0.2"  # Sweep: [0.2, 0.4, ..., 2.0]
algorithm = 4  # Leiden

Dependencies

Upstream Processes

• Required: SeuratClustering (or SeuratClusteringOfAllCells if TOrBCellSelection used)
• Optional: ScRepCombiningExpression (if TCR/BCR data present, adds clonality metadata for subsetting)
• Optional: CellTypeAnnotation (if using annotated cell types for subsetting)

Downstream Processes

• SeuratClusterStats: Statistics for sub-clusters
• ClusterMarkers: Differential expression between sub-clusters
• MarkersFinder: Flexible marker finding with enrichment analysis
• ScFGSEA: Pathway analysis on sub-cluster markers
• ModuleScoreCalculator: Module scoring within sub-clusters

Validation Rules

Subset Expression Validation

• Must be valid dplyr::filter() expression
• Can reference any metadata column in Seurat object
• Complex expressions supported: & (AND), | (OR), %in% (in operator)
• Example: seurat_clusters == 'c3' & percent.mt < 5
• Example: celltype %in% c('CD4 T cell', 'CD8 T cell')

Case Name Validation

• Must contain only alphanumeric characters
• Non-alphanumeric characters automatically removed
• Used as prefix: reductions and cluster names
• Avoid spaces, special characters in case names

Resolution Constraints

• Must be positive (resolution > 0)
• Single value, list, or range syntax allowed
• Range: "start:end:step" (step defaults to 0.1 if omitted)
• Multi-resolution creates multiple metadata columns

Dimension Requirements

• RunPCA.npcs must not exceed cells in subset
• RunUMAP.dims automatically truncated to min(dims, ncol(reduction) - 1)
• Use fewer dimensions for small subsets (< 100 cells)

Graph Name Consistency

• FindClusters.graph.name must match FindNeighbors output
• Default: pca_snn when not specified
• When using integrated reductions, ensure consistency

Troubleshooting

Issue: Subset Returns Zero Cells

Symptoms: Sub-clustering produces empty subset

Solutions:

1. Verify subset expression syntax

# Check if column exists and values are correct
[SeuratSubClustering.envs]
# Use single quotes for string comparison
subset = "seurat_clusters == 'c3'"  # Correct
subset = "seurat_clusters == c3"  # Wrong (treated as variable)

2. Verify column names exist in metadata

# Use existing columns only
subset = "seurat_clusters == 'c3'"  # seurat_clusters exists
subset = "cluster_id == 'c3'"  # cluster_id may not exist

3. Check for exact string matching

# Case-sensitive
subset = "celltype == 'CD4 T cell'"  # Exact match
subset = "celltype == 'CD4 T Cell'"  # Wrong case

Issue: Too Many Small Sub-clusters

Symptoms: Hundreds of tiny sub-clusters, many singletons

Solutions:

[SeuratSubClustering.envs.FindClusters]
resolution = 0.4  # Lower resolution
algorithm = 4  # Leiden handles singletons better

Issue: Sub-clusters Overlapping in UMAP

Symptoms: Poor separation in sub-cluster visualization

Solutions:

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

Issue: Sub-clustering Uses Wrong Reduction

Symptoms: Clustering on raw RNA instead of integrated data

Solutions:

[SeuratSubClustering.envs.FindNeighbors]
reduction = "integrated.cca"  # Use integrated reduction

[SeuratSubClustering.envs.RunUMAP]
reduction = "integrated.cca"

Issue: Multi-resolution Columns Not Created

Symptoms: Only final resolution column appears

Solutions:

[SeuratSubClustering.envs.FindClusters]
# Use list syntax (not single value with range)
resolution = [0.4, 0.6, 0.8, 1.0]  # Correct
resolution = "0.4:1.0:0.2"  # Also correct

Issue: Case Names Too Similar

Symptoms: Confusion between multiple cases

Solutions:

# Use descriptive, unique case names
[SeuratSubClustering.envs.cases]
T_CD4_Effector = {subset = "..."}
T_CD4_Naive = {subset = "..."}
B_Memory = {subset = "..."}

Issue: Sub-clustering on All Cells (Not Subset)

Symptoms: Default case runs on entire object

Solutions:

# Always specify subset or use cases
[SeuratSubClustering.envs]
subset = "seurat_clusters == 'c3'"  # Explicit subset

# Or define specific cases
[SeuratSubClustering.envs.cases.MyCase]
subset = "seurat_clusters == 'c3'"

Issue: Reductions Not Saved

Symptoms: Cannot find <CASENAME>PC_ or <CASENAME>UMAP_

Solutions:

# Ensure case name is alphanumeric only
[SeuratSubClustering.envs.cases]
MySubCluster1 = {subset = "..."}  # Correct
Sub-Cluster = {subset = "..."}  # Hyphen removed -> SubCluster

# Check metadata for actual reduction names
# Reductions are: <CASENAME>pc, <CASENAME>umap (lowercase)

Best Practices

1. Define explicit subsets: Always specify subset or define cases to avoid default case on all cells
2. Use descriptive case names: Make case names clear and unique (e.g., T_Effector, not case1)
3. Test multiple resolutions: Sweep resolution range to find optimal granularity for each subset
4. Use Leiden algorithm: Prefer algorithm = 4 for better community detection
5. Leverage metadata columns: Use CellTypeAnnotation results, TCR clonality, or custom mutaters for subsetting
6. Set random seeds: Ensure reproducible sub-clustering results with random.seed
7. Parallelize large subsets: Use ncores > 1 for subsets > 10k cells
8. Adjust UMAP parameters: Smaller subsets may need different n.neighbors and min.dist
9. Document sub-clustering strategy: Comment on biological rationale for each case in config
10. Use multi-resolution: Test [0.4, 0.6, 0.8, 1.0] to capture different granularities

Related Processes

• SeuratClustering: Initial clustering before sub-clustering
• SeuratClusteringOfAllCells: Clustering before T/B cell selection
• CellTypeAnnotation: Annotate clusters before sub-clustering by cell type
• ClusterMarkers: Find markers for sub-clusters
• MarkersFinder: Flexible marker finding with multiple comparison groups