Topexpressinggenesofallcells

Configuration Structure

Process Enablement

[TopExpressingGenesOfAllCells]
cache = true

Input Specification

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

Note: srtobj accepts the output from SeuratClusteringOfAllCells.

Environment Variables

Core Parameters

[TopExpressingGenesOfAllCells.envs]
# Number of top expressing genes to identify per cluster
n = 250

# Enrichment style
enrich_style = "enrichr"  # Options: "enrichr", "clusterprofiler"

# Enrichment databases
dbs = ["KEGG_2021_Human", "MSigDB_Hallmark_2020"]

Enrichment Plot Settings

[TopExpressingGenesOfAllCells.envs.enrich_plots_defaults]
# Plot type for enrichment results
plot_type = "bar"  # Options: "bar", "dot", "lollipop", "network", "enrichmap", "wordcloud"

# Device parameters
devpars = {res = 100, width = 800, height = 600}

# Additional output formats
more_formats = []

# Save R code to reproduce plots
save_code = false

# Top terms to display
top_term = 10  # Number of top enriched pathways to show
ncol = 1  # Number of columns in multi-panel plots

Cell Subsetting

[TopExpressingGenesOfAllCells.envs]
# Subset cells before analysis (optional)
subset = ""

Cache Control

[TopExpressingGenesOfAllCells.envs]
# Cache intermediate results
cache = "/tmp"  # true, false, or directory path

Configuration Examples

Minimal Configuration

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

Top 10 Genes for Broad Cell Type ID

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TopExpressingGenesOfAllCells.envs]
n = 10
dbs = ["MSigDB_Hallmark_2020"]

Multiple Databases for Comprehensive Overview

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TopExpressingGenesOfAllCells.envs]
n = 100
dbs = [
  "KEGG_2021_Human",
  "MSigDB_Hallmark_2020",
  "GO_Biological_Process_2025"
]

Common Patterns

Pattern 1: Quick All-Cell Overview (Pre-Selection)

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TopExpressingGenesOfAllCells.envs]
n = 10
dbs = ["MSigDB_Hallmark_2020"]

[TopExpressingGenesOfAllCells.envs.enrich_plots_defaults]
plot_type = "bar"
top_term = 10

What to expect: Top 10 genes per cluster showing broad cell type markers (CD3 for T cells, CD19 for B cells, CD14 for monocytes, etc.)

Pattern 2: Broad Cell Type Signature Identification

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TopExpressingGenesOfAllCells.envs]
n = 50

[TopExpressingGenesOfAllCells.envs.enrich_plots]
"T Cell Pathways" = {plot_type = "bar", dbs = ["KEGG_2021_Human"]}
"B Cell Pathways" = {plot_type = "bar", dbs = ["KEGG_2021_Human"]}
"Myeloid Pathways" = {plot_type = "bar", dbs = ["KEGG_2021_Human"]}

What to expect: Identification of T cell (CD3E, CD3D), B cell (CD19, MS4A1), and myeloid (CD14, LYZ) signatures across clusters

Pattern 3: Quality Check for Unexpected Cell Types

[TopExpressingGenesOfAllCells]

[TopExpressingGenesOfAllCells.in]
srtobj = ["SeuratClusteringOfAllCells"]

[TopExpressingGenesOfAllCells.envs]
n = 20
dbs = [
  "GO_Biological_Process_2025",
  "GO_Cellular_Component_2025"
]

[TopExpressingGenesOfAllCells.envs.enrich_plots_defaults]
plot_type = "dot"
top_term = 15

What to expect: Detection of contamination (e.g., EPCAM for epithelial, COL1A1 for fibroblasts, RBC markers)

Difference from TopExpressingGenes

TopExpressingGenesOfAllCells vs TopExpressingGenes:

Workflow context:

RNA Input → SeuratPreparing → SeuratClusteringOfAllCells
                                              ↓
                                      TopExpressingGenesOfAllCells  ← Runs here
                                              ↓
                                      TOrBCellSelection (separates T/B)
                                              ↓
                                      SeuratClustering (on selected cells)
                                              ↓
                                      TopExpressingGenes  ← Runs here

Recommendation:

• Use TopExpressingGenesOfAllCells to assess overall data quality and cell type composition
• Use TopExpressingGenes for detailed analysis of T or B cell subtypes
• Enable both for comprehensive analysis: pre-selection overview + post-selection deep dive

Dependencies

• Upstream: SeuratClusteringOfAllCells
• Downstream: TOrBCellSelection (optional - this process provides pre-selection context)
• Data: Seurat object with cluster assignments for ALL cells

Validation Rules

• n parameter: Must be positive integer (typically 10-500)
• dbs: Must be valid enrichit/Enrichr database names or local GMT file paths
• enrich_style: Must be "enrichr" or "clusterprofiler"
• plot_type: Must be valid scplotter plot type
• Workflow requirement: Only runs when SeuratClusteringOfAllCells is enabled

Troubleshooting

Process Not Running

Issue: TopExpressingGenesOfAllCells not executed despite being in config

Causes:

• SeuratClusteringOfAllCells not enabled
• Missing dependency in workflow
• Process disabled via validation warning

Solutions:

1. Ensure SeuratClusteringOfAllCells is enabled in config
2. Check validation warnings: python -m immunopipe.validate_config config.toml
3. Verify both processes in config:

   [SeuratClusteringOfAllCells]
   [TopExpressingGenesOfAllCells]
   

Mixed Cell Types in Results

Issue: Clusters show multiple cell type markers (CD3 + CD19)

Causes:

• Overlapping clusters (resolution too low)
• Doublets/multiplets not filtered
• Contamination in data

Solutions:

1. Adjust clustering resolution in SeuratClusteringOfAllCells
2. Filter doublets in SeuratPreparing step
3. Use TOrBCellSelection after assessment to clean data

No Clear Cell Type Signatures

Issue: Top genes list lacks expected markers (CD3, CD19, CD14)

Causes:

• Data quality issues (low counts, high mitochondrial)
• Wrong organism (human vs mouse gene symbols)
• Incomplete clustering

Solutions:

1. Check QC metrics in SeuratClusterStatsOfAllCells
2. Verify organism (uppercase=human, titlecase=mouse)
3. Review clustering results from SeuratClusteringOfAllCells

Ribosomal/Mitochondrial Gene Dominance

Issue: Top genes list dominated by housekeeping genes (RPS, RPL, MT-)

Solutions:

1. Increase n parameter to see beyond housekeeping genes
2. Filter out ribosomal/mitochondrial genes in SeuratPreparing step
3. Use ClusterMarkersOfAllCells for differential expression

Empty Enrichment Results

Issue: No pathways enriched despite top genes identified

Causes:

• Gene identifiers don't match database
• n too small for meaningful enrichment
• Database not appropriate for cell type

Solutions:

1. Increase n to 100-500 genes
2. Verify species match (check gene symbols)
3. Try different databases (e.g., GO_Biological_Process_2025)

Plot Rendering Errors

Issue: Enrichment plots fail to render

Causes:

• Network plots with too many terms
• Missing dependencies in R environment

Solutions:

1. Reduce top_term parameter
2. Use simpler plot types (bar, dot)
3. Verify R packages installed: enrichit, scplotter

Output Structure

<srtobj_stem>.top_expressing_genes/
├── <cluster_name>/              # One subdirectory per cluster (ALL cells)
│   ├── top_genes.tsv            # Top N genes with expression metrics
│   └── enrich/                 # Enrichment results
│       ├── <db_name>/          # One subdirectory per database
│       │   ├── *.Bar-Plot.png  # Enrichment plots
│       │   ├── *.enrich.tsv    # Enrichment tables
│       │   └── ...

External References

Enrichment Databases (enrichit)

Full reference

Built-in databases:

• KEGG_2021_Human - KEGG pathways (human)
• MSigDB_Hallmark_2020 - MSigDB Hallmark gene sets
• GO_Biological_Process_2025 - GO Biological Process terms
• GO_Cellular_Component_2025 - GO Cellular Component terms
• GO_Molecular_Function_2025 - GO Molecular Function terms
• Reactome_Pathways_2024 - Reactome pathways
• WikiPathways_2024_Human - WikiPathways (human)

Enrichr libraries: See https://maayanlab.cloud/Enrichr/#libraries

Enrichment Plot Types (scplotter)

Full reference

• bar - Bar chart of enriched terms
• dot - Dot plot (bubble chart)
• lollipop - Lollipop plot
• network - Network visualization of term relationships
• enrichmap - Enrichment map (similar to Cytoscape)
• wordcloud - Word cloud visualization

Enrichment Styles

• enrichr - Fisher's exact test (Enrichr-style)
• clusterprofiler - Hypergeometric test (clusterProfiler-style)

See Also

• TopExpressingGenes - Top genes for selected T/B cells after selection
• ClusterMarkersOfAllCells - Differential expression for all cells before selection
• SeuratClusteringOfAllCells - Clustering on all cells before T/B selection
• TOrBCellSelection - T/B cell separation process