TopExpressingGenes Process Configuration

Purpose

Identifies and visualizes the top expressing genes per cluster in T/B cells, followed by pathway enrichment analysis. Provides quick cluster characterization by highlighting the most highly expressed genes and their biological functions.

When to Use

• After: SeuratClustering and TOrBCellSelection processes
• Use cases: Quick cluster characterization, identifying dominant gene programs, pathway enrichment
• Optional process: Enable only when cluster-level expression profiling is needed

Configuration Structure

Process Enablement

[TopExpressingGenes]
cache = true

Input Specification

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

Note: srtobj accepts the output from SeuratClustering or SeuratSubClustering.

Environment Variables

Core Parameters

[TopExpressingGenes.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

[TopExpressingGenes.envs.enrich_plots_defaults]
# Plot type: "bar", "dot", "lollipop", "network", "enrichmap", "wordcloud"
plot_type = "bar"
devpars = {res = 100, width = 800, height = 600}
top_term = 10  # Top enriched pathways to show
ncol = 1

Configuration Examples

Minimal Configuration

[TopExpressingGenes]

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

Top 10 Genes with Custom Databases

[TopExpressingGenes]

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

[TopExpressingGenes.envs]
n = 10
dbs = ["GO_Biological_Process_2025", "Reactome_Pathways_2024"]

Network Visualization

[TopExpressingGenes.envs.enrich_plots."Network"]
plot_type = "network"
top_term = 15

[TopExpressingGenes.envs.enrich_plots."Enrichmap"]
plot_type = "enrichmap"

Common Patterns

Pattern 1: Quick Cluster Overview

[TopExpressingGenes]

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

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

Pattern 2: Detailed Profile

[TopExpressingGenes.envs]
n = 250
enrich_style = "clusterprofiler"

[TopExpressingGenes.envs.enrich_plots]
"KEGG" = {plot_type = "bar", dbs = ["KEGG_2021_Human"]}
"Reactome" = {plot_type = "network"}

Pattern 3: Multiple Visualizations

[TopExpressingGenes.envs]
n = 50

[TopExpressingGenes.envs.enrich_plots."Bar"]
plot_type = "bar"

[TopExpressingGenes.envs.enrich_plots."Word Cloud"]
plot_type = "wordcloud"

Difference from ClusterMarkers

Recommendation: Use both processes:

1. TopExpressingGenes: Quick overview of dominant programs
2. ClusterMarkers: Rigorous marker identification

Dependencies

• Upstream: SeuratClustering, TOrBCellSelection (for TCR route)
• Downstream: None (terminal analysis process)

Validation Rules

• n: Positive integer (typically 10-500)
• dbs: Valid enrichit/Enrichr database names or local GMT paths
• enrich_style: "enrichr" or "clusterprofiler"
• plot_type: Valid scplotter plot type

Troubleshooting

Ribosomal/Mitochondrial Gene Dominance

Issue: Housekeeping genes (RPS, RPL, MT-) dominate

Solutions: Increase n, use ClusterMarkers, filter genes in SeuratPreparing

Empty Enrichment Results

Issue: No pathways enriched

Solutions: Increase n to 100-500, verify species (UPPERCASE=human, TitleCase=mouse)

Plot Rendering Errors

Issue: Plots fail to render

Solutions: Reduce top_term (5-15), use simpler plots (bar, dot)

Performance Issues

Issue: Process too slow

Solutions: Reduce n, use fewer databases, disable enrichment: dbs = []

External References

Databases (enrichit)

• KEGG_2021_Human - KEGG pathways
• MSigDB_Hallmark_2020 - Hallmark gene sets
• GO_Biological_Process_2025 - GO Biological Process
• Reactome_Pathways_2024 - Reactome pathways
• See: https://pwwang.github.io/enrichit/reference/FetchGMT.html

Plot Types (scplotter)

• bar - Bar chart
• dot - Dot plot
• lollipop - Lollipop plot
• network - Network visualization
• enrichmap - Enrichment map
• wordcloud - Word cloud

Enrichment Styles

• enrichr - Fisher's exact test
• clusterprofiler - Hypergeometric test

See Also

• TopExpressingGenesOfAllCells - Top genes before T/B selection
• ClusterMarkers - Differential expression analysis