ModuleScoreCalculator Process Configuration

Purpose: Calculate module/pathway/gene signature scores per cell using Seurat's AddModuleScore or CellCycleScoring functions.

When to Use

• To score cells for specific gene programs (exhaustion, cytotoxicity, proliferation)
• For pathway activity analysis using curated gene sets
• To quantify functional states (activation, differentiation, memory)
• To add diffusion map components for trajectory analysis
• For cell cycle scoring to identify S and G2M phase cells

Configuration Structure

Process Enablement

[ModuleScoreCalculator]
cache = true

Input Specification

[ModuleScoreCalculator.in]
# Input: Seurat object from SeuratClustering
srtobj = ["SeuratClustering"]

Environment Variables

[ModuleScoreCalculator.envs]
# Default parameters inherited by all modules
defaults = { nbin = 24, ctrl = 100, seed = 8525, agg = "mean" }

# Module definitions (key = module name, value = gene set parameters)
modules = {}

# Post-scoring metadata transformations
post_mutaters = {}

External References

Seurat AddModuleScore Parameters

Parameter	Type	Default	Description
features	string/list	Required	Gene names or cc.genes/cc.genes.updated.2019 for cell cycle
nbin	int	24	Number of bins for aggregate expression levels of all analyzed features
ctrl	int	100	Number of control features selected from same bin per analyzed feature
k	boolean	false	Use feature clusters from DoKMeans instead of random selection
assay	string	NULL	The assay to use (defaults to active assay)
seed	int	8525	Random seed for reproducibility
search	boolean	false	Search for symbol synonyms if features don't match
keep	boolean	false	Keep individual feature scores (non-cell cycle only)
agg	string	"mean"	Aggregation function: mean, median, sum, max, min, var, sd

Reference: https://satijalab.org/seurat/reference/addmodulescore

CellCycleScoring Parameters

When using features = "cc.genes" or "cc.genes.updated.2019", adds:

• S.Score - S phase score per cell
• G2M.Score - G2M phase score per cell
• Phase - Cell cycle phase assignment (G1, S, G2M)

Reference: https://satijalab.org/seurat/reference/cellcyclescoring

Diffusion Map Parameters

{"DC": {"features": 2, "kind": "diffmap"}}

Adds first N diffusion components as metadata columns (DC_1, DC_2, ...).

Reference: https://www.rdocumentation.org/packages/destiny/versions/2.0.4/topics/DiffusionMap

Configuration Examples

Minimal Configuration

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

Cell Cycle Scoring

[ModuleScoreCalculator.envs.modules]
CellCycle = { features = "cc.genes.updated.2019" }

Output columns: S.Score, G2M.Score, Phase

Exhaustion Score (T Cells)

[ModuleScoreCalculator.envs.modules.Exhaustion]
features = "HAVCR2,ENTPD1,LAYN,LAG3,TIGIT,PDCD1,TOX"

Cytotoxicity Score (CD8+ T Cells, NK Cells)

[ModuleScoreCalculator.envs.modules.Cytotoxicity]
features = "GZMB,PRF1,NKG7,GNLY,CTSW"

Proliferation Score

[ModuleScoreCalculator.envs.modules.Proliferation]
features = "MKI67,STMN1,TUBB,PCNA,TOP2A"

Activation Score

[ModuleScoreCalculator.envs.modules.Activation]
features = "IFNG,TNF,CD69,CD25"

Multiple Gene Sets (Functional States)

[ModuleScoreCalculator.envs.modules]
[ModuleScoreCalculator.envs.modules.CellCycle]
features = "cc.genes.updated.2019"

[ModuleScoreCalculator.envs.modules.Exhaustion]
features = "HAVCR2,ENTPD1,LAYN,LAG3,TIGIT,PDCD1"

[ModuleScoreCalculator.envs.modules.Activation]
features = "IFNG,TNF,CD69,CD25"

[ModuleScoreCalculator.envs.modules.Proliferation]
features = "MKI67,STMN1,TUBB,PCNA"

Diffusion Map Components

[ModuleScoreCalculator.envs.modules]
DC = { features = 2, kind = "diffmap" }

Use with: env.dimplots in SeuratClusterStats with reduction = "DC"

Post-Metadata Transformation

[ModuleScoreCalculator.envs.post_mutaters]
# Calculate combined exhaustion-activation ratio
Exh_Act_Ratio = "Exhaustion1 / Activation1"

# Classify high vs low exhaustion
Exhaustion_Level = "ifelse(Exhaustion1 > median(Exhaustion1, na.rm = TRUE), 'High', 'Low')"

Common Patterns

Pattern 1: T Cell Functional States

[ModuleScoreCalculator.envs.modules]
# Exhaustion markers (checkpoint genes)
Exhaustion = {
    features = "HAVCR2,ENTPD1,LAYN,LAG3,TIGIT,PDCD1,TOX,CTLA4"
}

# Activation markers
Activation = {
    features = "IFNG,TNF,CD69,CD25,IL2RA"
}

# Memory markers
Memory = {
    features = "IL7R,CCR7,SELL,S100A4"
}

# Terminal differentiation
Terminal_Diff = {
    features = "TIGIT,PDCD1,CD274,CD244,CD160"
}

Pattern 2: NK Cell Functional States

[ModuleScoreCalculator.envs.modules]
# Cytotoxicity
Cytotoxicity = {
    features = "GZMB,PRF1,NKG7,GNLY,CTSW"
}

# Activation
NK_Activation = {
    features = "NCAM1,KLRD1,FCGR3A"
}

# Exhaustion
NK_Exhaustion = {
    features = "HAVCR2,LAG3,PDCD1,TIGIT"
}

Pattern 3: Cell Cycle with Custom Parameters

[ModuleScoreCalculator.envs.defaults]
nbin = 24
ctrl = 100
seed = 8525

[ModuleScoreCalculator.envs.modules]
CellCycle = {
    features = "cc.genes.updated.2019"
}

Pattern 4: Metabolic Pathway Scores

[ModuleScoreCalculator.envs.modules]
# Glycolysis (Warburg effect)
Glycolysis = {
    features = "HK2,PKM,LDHA,PFKL,ENO1"
}

# Oxidative phosphorylation
OXPHOS = {
    features = "ND1,ND2,ND3,COX1,COX2,ATP5A1"
}

# Fatty acid oxidation
FAO = {
    features = "CPT1A,ACOX1,HADHA"
}

Pattern 5: B Cell Functionality

[ModuleScoreCalculator.envs.modules]
# Plasma cell differentiation
Plasma = {
    features = "MZB1,SSR4,SDC1,XBP1,PRDM1"
}

# Germinal center
Germinal_Center = {
    features = "BCL6,AICDA,MEF2B"
}

# Naive vs memory
Naive = {
    features = "IL7R,CCR7,IGHD"
}
Memory = {
    features = "CD27,IGG1,IGHG1"
}

Gene Set Resources

MSigDB (Molecular Signatures Database)

• URL: https://www.gsea-msigdb.org/
• Hallmark Collection: 50 curated gene sets for biological processes
  • HALLMARK_INTERFERON_GAMMA_RESPONSE
  • HALLMARK_TNFA_SIGNALING_VIA_NFKB
  • HALLMARK_INFLAMMATORY_RESPONSE
  • HALLMARK_HYPOXIA
  • HALLMARK_APOPTOSIS
• Immunologic Signatures (C7): Gene sets from immunology studies
• Download: Available in GMT format for direct use

CellMarker Database

• URL: http://bioinfo.life.hust.edu.cn/CellMarker/
• Cell type-specific markers for human and mouse

Literature-Derived Signatures

T Cell Exhaustion Markers:

• Primary: HAVCR2 (TIM-3), PDCD1 (PD-1), LAG3, TIGIT, CTLA4
• Transcription factors: TOX, NR4A1, EOMES

T Cell Activation Markers:

• Cytokines: IFNG, TNF, IL2
• Surface: CD69, CD25 (IL2RA), CD38

Cytotoxicity Markers:

• Granzymes: GZMB, GZMA, GZMH
• Perforin: PRF1
• NK receptors: NKG7, GNLY, CTSW

Proliferation Markers:

• Ki-67: MKI67
• Tubulin: STMN1, TUBB
• PCNA: PCNA, TOP2A

Cell Cycle Genes (Seurat built-in):

• cc.genes - Original Tirosh et al. 2016 gene set
• cc.genes.updated.2019 - Updated with 2019 gene symbols

Dependencies

Upstream Processes

• Required: SeuratClustering - Provides the Seurat object
• Optional: TOrBCellSelection - If working with T/B cell subsets

Downstream Processes

• SeuratClusterStats - Visualize module scores across clusters
• CellCellCommunication - Correlate scores with cell interactions
• ScFGSEA - Validate module activity with enrichment analysis

Validation Rules

Gene Set Format Validation

• Comma-separated strings: "GENE1,GENE2,GENE3" ✓
• Cell cycle keywords: "cc.genes" or "cc.genes.updated.2019" ✓
• Diffusion map: {"features": N, "kind": "diffmap"} ✓

Gene Name Matching

• Human genes: Uppercase (MKI67, IFNG) ✓
• Mouse genes: Title case (Mki67, Ifng) ✓
• Search mode: Set search = true to automatically find synonyms
• Keep mode: Set keep = true to retain unmatched features

Parameter Constraints

• nbin: Typically 10-50 (default 24)
• ctrl: Typically 10-500 (default 100)
• Minimum genes: ≥5 genes recommended for robust scoring
• Maximum genes: No hard limit, but performance may degrade >1000 genes

Troubleshooting

Issue: Genes Not Found in Object

Symptom: Warning "XX% of features not found in object"

Solutions:

1. Check gene name format (uppercase for human, title case for mouse)
2. Enable search = true to find symbol synonyms
3. Verify gene symbols match your Seurat object's row names
4. Use search = true + keep = true to debug missing genes

Issue: Too Few Genes in Set

Symptom: Module score is NA or unreliable

Solutions:

1. Ensure ≥5 genes in gene set for robust scoring
2. Add alternative markers to expand gene set
3. Check if genes are expressed in your dataset
4. Use keep = true to see how many genes matched

Issue: Cell Cycle Score All G1

Symptom: Most cells classified as G1 phase

Solutions:

1. Check if cells are truly non-proliferating (e.g., memory T cells)
2. Verify data quality (low UMI counts may obscure cell cycle)
3. Consider using cc.genes instead of cc.genes.updated.2019
4. Check S.Score and G2M.Score values directly

Issue: Module Scores All Similar

Symptom: No variation in scores across cells

Solutions:

1. Genes may be uniformly expressed or not detected
2. Try adjusting nbin and ctrl parameters
3. Verify assay selection (assay = "RNA" vs "SCT")
4. Check if cells express the expected markers

Issue: Diffusion Map Components Not Added

Symptom: DC_1, DC_2 columns missing

Solutions:

1. Ensure destiny R package is installed
2. Verify SingleCellExperiment package is available
3. Use correct format: {"DC": {"features": 2, "kind": "diffmap"}}
4. Requires R packages: SingleCellExperiment, destiny

Best Practices

Gene Set Selection

• Use literature-validated signatures when possible
• Combine complementary markers (e.g., exhaustion: HAVCR2 + PDCD1 + LAG3)
• Consider species-specific marker expression patterns
• Test gene sets on a subset before full pipeline run

Parameter Tuning

• nbin = 24: Default works well for most datasets
• ctrl = 100: Increase if many genes have similar expression levels
• seed = 8525: Keep fixed for reproducibility across runs
• agg = "mean": Use median for outlier-resistant aggregation

Visualization

• Use SeuratClusterStats.envs.dimplots to visualize scores
• Add to SeuratClusterStats.envs.violins for distribution plots
• Correlate scores with clusters or annotations
• Consider post_mutaters for custom score transformations

Performance

• Module scoring is computationally cheap (<5 min for typical datasets)
• Larger gene sets (>1000 genes) may take longer
• Diffusion map computation scales with cell number (O(n²))

Integration Example

# Complete workflow with multiple scores
[ModuleScoreCalculator.envs.defaults]
nbin = 24
ctrl = 100
seed = 8525

[ModuleScoreCalculator.envs.modules]
# Cell cycle
CellCycle = { features = "cc.genes.updated.2019" }

# T cell function
Exhaustion = { features = "HAVCR2,ENTPD1,LAYN,LAG3,TIGIT,PDCD1,TOX,CTLA4" }
Activation = { features = "IFNG,TNF,CD69,CD25" }
Memory = { features = "IL7R,CCR7,SELL,S100A4" }

# Cytotoxicity
Cytotoxicity = { features = "GZMB,PRF1,NKG7,GNLY" }

# Metabolism
Glycolysis = { features = "HK2,PKM,LDHA,PFKL,ENO1" }

[ModuleScoreCalculator.envs.post_mutaters]
# Classify T cell states
Tcell_State = """
case_when(
    Exhaustion1 > median(Exhaustion1, na.rm = TRUE) ~ 'Exhausted',
    Activation1 > median(Activation1, na.rm = TRUE) ~ 'Activated',
    Memory1 > median(Memory1, na.rm = TRUE) ~ 'Memory',
    TRUE ~ 'Naive'
)
"""

# Combined functional score
Functionality = "(Activation1 + Cytotoxicity1) / (Exhaustion1 + 1)"

Notes

• Process is optional: Only runs when [ModuleScoreCalculator] section exists in config
• Multiple modules: Define any number of modules in modules dictionary
• Column naming: Scores stored as ModuleName1, ModuleName2, etc.
• Cell cycle special case: Uses CellCycleScoring() which adds S.Score, G2M.Score, Phase
• Diffusion map: Special module type for trajectory analysis
• Post-processing: Use post_mutaters for custom metadata calculations
• Visualization: Scores available in SeuratClusterStats for plotting

Quick Reference

Gene Set Formats:

# Comma-separated
features = "GENE1,GENE2,GENE3"

# Cell cycle (built-in)
features = "cc.genes.updated.2019"

# Diffusion map (special)
features = 2
kind = "diffmap"

Common Gene Sets:

Exhaustion = "HAVCR2,PDCD1,LAG3,TIGIT,CTLA4,TOX"
Cytotoxicity = "GZMB,PRF1,NKG7,GNLY"
Proliferation = "MKI67,STMN1,PCNA,TOP2A"
Activation = "IFNG,TNF,CD69,CD25"
Memory = "IL7R,CCR7,SELL"

Process Location: /immunopipe/processes.py (line 455) Documentation: /docs/processes/ModuleScoreCalculator.md Function: Seurat::AddModuleScore(), Seurat::CellCycleScoring()