メニュー
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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Master skill for generating immunopipe pipeline configurations. Determines pipeline architecture based on data type (scRNA-seq with or without scTCR/BCR-seq) and analysis requirements. Routes to individual process skills for detailed configuration. Use this skill when starting a new immunopipe configuration or modifying pipeline-level options.
Analyzes physicochemical properties of CDR3 amino acid sequences to understand biochemical characteristics of T-cell receptor repertoires. Performs regression analysis between two cell groups at different CDR3 lengths for each physicochemical feature (hydrophobicity, volume, isoelectric point, etc.).
Cluster TCR/BCR clones by CDR3 sequences using GIANA or ClusTCR (both Faiss-based). Adds `CDR3_Cluster` column to metadata for clonotype analysis.
Infer ligand-receptor interactions and cell-cell communication networks from single-cell RNA-seq data using the LIANA+ framework. Identifies potential signaling events between cell types based on gene expression patterns and curated ligand-receptor interaction databases.
Visualize cell-cell communication inference results from CellCellCommunication process. Creates publication-ready network diagrams, heatmaps, and interaction plots to help interpret ligand-receptor interactions between cell types.
Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assigning meaningful biological identities to each cluster.
| 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. |
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.
SeuratClustering and TOrBCellSelection processes[TopExpressingGenes]
cache = true
[TopExpressingGenes.in]
srtobj = ["SeuratClustering"]
Note: srtobj accepts the output from SeuratClustering or SeuratSubClustering.
[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"]
[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
[TopExpressingGenes]
[TopExpressingGenes.in]
srtobj = ["SeuratClustering"]
[TopExpressingGenes]
[TopExpressingGenes.in]
srtobj = ["SeuratClustering"]
[TopExpressingGenes.envs]
n = 10
dbs = ["GO_Biological_Process_2025", "Reactome_Pathways_2024"]
[TopExpressingGenes.envs.enrich_plots."Network"]
plot_type = "network"
top_term = 15
[TopExpressingGenes.envs.enrich_plots."Enrichmap"]
plot_type = "enrichmap"
[TopExpressingGenes]
[TopExpressingGenes.in]
srtobj = ["SeuratClustering"]
[TopExpressingGenes.envs]
n = 10
dbs = ["MSigDB_Hallmark_2020"]
[TopExpressingGenes.envs]
n = 250
enrich_style = "clusterprofiler"
[TopExpressingGenes.envs.enrich_plots]
"KEGG" = {plot_type = "bar", dbs = ["KEGG_2021_Human"]}
"Reactome" = {plot_type = "network"}
[TopExpressingGenes.envs]
n = 50
[TopExpressingGenes.envs.enrich_plots."Bar"]
plot_type = "bar"
[TopExpressingGenes.envs.enrich_plots."Word Cloud"]
plot_type = "wordcloud"
| 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:
TopExpressingGenes: Quick overview of dominant programsClusterMarkers: Rigorous marker identificationSeuratClustering, TOrBCellSelection (for TCR route)n: Positive integer (typically 10-500)dbs: Valid enrichit/Enrichr database names or local GMT pathsenrich_style: "enrichr" or "clusterprofiler"plot_type: Valid scplotter plot typeIssue: Housekeeping genes (RPS, RPL, MT-) dominate
Solutions: Increase n, use ClusterMarkers, filter genes in SeuratPreparing
Issue: No pathways enriched
Solutions: Increase n to 100-500, verify species (UPPERCASE=human, TitleCase=mouse)
Issue: Plots fail to render
Solutions: Reduce top_term (5-15), use simpler plots (bar, dot)
Issue: Process too slow
Solutions: Reduce n, use fewer databases, disable enrichment: dbs = []
KEGG_2021_Human - KEGG pathwaysMSigDB_Hallmark_2020 - Hallmark gene setsGO_Biological_Process_2025 - GO Biological ProcessReactome_Pathways_2024 - Reactome pathwaysbar - Bar chartdot - Dot plotlollipop - Lollipop plotnetwork - Network visualizationenrichmap - Enrichment mapwordcloud - Word cloudenrichr - Fisher's exact testclusterprofiler - Hypergeometric testTopExpressingGenesOfAllCells - Top genes before T/B selectionClusterMarkers - Differential expression analysis