TopExpressingGenesOfAllCells¶

Top expressing genes for clusters of all cells.

Input¶

• srtobj: The seurat object in RDS or qs/qs2 format

Output¶

• outdir: Default: {{in.srtobj | stem}}.top_expressing_genes.
  The output directory for the tables and plots

Environment Variables¶

• ncores (type=int): Default: 1.
  Number of cores to use for reading and writing the seurat object.
  
• dbs (list): Default: ['KEGG_2021_Human', 'MSigDB_Hallmark_2020'].
  The dbs to do enrichment analysis for significant markers.
  You can use built-in dbs in enrichit, or provide your own gmt files.
  See also https://pwwang.github.io/enrichit/reference/FetchGMT.html.
  The built-in dbs include:
  
  • "BioCarta" or "BioCarta_2016"
  • "GO_Biological_Process" or "GO_Biological_Process_2025"
  • "GO_Cellular_Component" or "GO_Cellular_Component_2025"
  • "GO_Molecular_Function" or "GO_Molecular_Function_2025"
  • "KEGG", "KEGG_Human", "KEGG_2021", or "KEGG_2021_Human"
  • "Hallmark", "MSigDB_Hallmark", or "MSigDB_Hallmark_2020"
  • "Reactome", "Reactome_Pathways", or "Reactome_Pathways_2024"
  • "WikiPathways", "WikiPathways_2024", "WikiPathways_Human", or "WikiPathways_2024_Human" You can also fetch more dbs from https://maayanlab.cloud/Enrichr/#libraries.
    
• n (type=int): Default: 250.
  The number of top expressing genes to find.
  
• enrich_style (choice): Default: enrichr.
  The style of the enrichment analysis.
  The enrichment analysis will be done by EnrichIt() from enrichit.
  Two styles are available:
  
  • enrichr: enrichr style enrichment analysis (fisher's exact test will be used).
    
  • clusterprofiler: clusterProfiler style enrichment analysis (hypergeometric test will be used).
    
  • clusterProfiler: alias for clusterprofiler
• enrich_plots_defaults (ns): Default options for the plots to generate for the enrichment analysis.
  
  • plot_type: The type of the plot.
    See https://pwwang.github.io/scplotter/reference/EnrichmentPlot.html.
    Available types are bar, dot, lollipop, network, enrichmap and wordcloud.
    
  • descr: A description of the plot to be shown above the plot image.
    
  • more_formats (type=list): Default: [].
    The extra formats to save the plot in.
    
  • save_code (flag): Default: False.
    Whether to save the code to generate the plot.
    
  • devpars (ns): The device parameters for the plots.
    
    • res (type=int): Default: 100.
      The resolution of the plots.
      
    • height (type=int): The height of the plots.
      
    • width (type=int): The width of the plots.
      
  • <more>: See https://pwwang.github.io/scplotter/reference/EnrichmentPlot.htmll.
    
• enrich_plots (type=json): Default: {'Bar Plot': Diot({'plot_type': 'bar', 'ncol': 1, 'top_term': 10})}.
  Cases of the plots to generate for the enrichment analysis.
  The keys are the names of the cases and the values are the dicts inherited from enrich_plots_defaults.
  The cases under envs.cases can inherit this options.
  
• subset: An expression to subset the cells for each case.
  
• error (flag): Default: False.
  Stop the job if errors happen.
  Helpful when no/not enough markers are found or no pathways are enriched.
  If False, empty results will be returned.
  

SeeAlso¶

• TopExpressingGenes
• ClusterMarkers for examples of enrichment plots

Description¶

Finds the top expressing genes of every cluster found by SeuratClusteringOfAllCells and runs an enrichment analysis on them.
The top expressing genes are computed by Seurat and the enrichment is done by enrichr.

Base class¶

biopipen.ns.scrna.TopExpressingGenes

Deviations¶

cases is empty in the base, so the base computes no case by default; immunopipe defines the Cluster case explicitly.