topexpressinggenes

name	topexpressinggenes
description	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.

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

Aspect	TopExpressingGenes	ClusterMarkers
Finds	Highest expressed genes within clusters	Genes differentially expressed between clusters
Meaning	Basal/dominant expression	Distinguishing markers
Stat test	None (average expression)	Statistical (Wilcoxon, MAST)
Use case	Cluster identity/function	Marker discovery
Output	Top N genes	DEGs with p-values/FC

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