omicverse.single.pySCSA

omicverse.single.pySCSA#

class omicverse.single.pySCSA(adata, foldchange=1.5, pvalue=0.05, output='temp/rna_anno.txt', model_path='', outfmt='txt', Gensymbol=True, species='Human', weight=100, tissue='All', target='cellmarker', celltype='normal', norefdb=False, cellrange=None, noprint=True, list_tissue=False, tissuename=None, speciename=None)[source]#

Automated cell-type annotation using SCSA marker-enrichment scoring.

Parameters:

  • adata (anndata.AnnData) – Query AnnData for cell-type annotation.

  • foldchange (float, optional, default=1.5) – Fold-change cutoff for marker filtering.

  • pvalue (float, optional, default=0.05) – P-value cutoff for marker filtering.

  • output (str, optional, default='temp/rna_anno.txt') – Output path for SCSA annotation report.

  • model_path (str, optional, default='') – Path to local SCSA database/model.

  • outfmt (str, optional, default='txt') – Output format for intermediate annotation report.

  • Gensymbol (bool, optional, default=True) – Whether gene symbols are used as identifiers.

  • species (str, optional, default='Human') – Species used for marker database matching.

  • weight (int, optional, default=100) – Marker-weight scaling factor used by SCSA scoring.

  • tissue (str, optional, default='All') – Tissue filter for marker database query.

  • target (str, optional, default='cellmarker') – Marker database target (for example 'cellmarker' or 'panglaodb').

  • celltype (str, optional, default='normal') – Annotation context/type mode used by SCSA.

  • norefdb (bool, optional, default=False) – If True, skip reference database matching.

  • cellrange (str, optional, default=None) – Optional range/filter for cell selection.

  • noprint (bool, optional, default=True) – If True, suppress verbose console output.

  • list_tissue (bool, optional, default=False) – If True, list available tissues and exit.

  • tissuename (str, optional, default=None) – Compatibility alias for tissue.

  • speciename (str, optional, default=None) – Compatibility alias for species.

Returns:

Initializes SCSA annotation settings and database options.

Return type:

None

Examples

>>> # CRITICAL: Use clustertype='leiden', NOT cluster='leiden'!
__init__(adata, foldchange=1.5, pvalue=0.05, output='temp/rna_anno.txt', model_path='', outfmt='txt', Gensymbol=True, species='Human', weight=100, tissue='All', target='cellmarker', celltype='normal', norefdb=False, cellrange=None, noprint=True, list_tissue=False, tissuename=None, speciename=None)[source]#

Initialize SCSA annotation workflow configuration.

Parameters:

  • adata (anndata.AnnData) – Query AnnData object.

  • foldchange (float) – Fold-change threshold used for marker filtering.

  • pvalue (float) – P-value threshold used for marker filtering.

  • output (str) – Output path of annotation report.

  • model_path (str) – Local SCSA database path. If empty, downloads default database.

  • outfmt (str) – Output format for SCSA report.

  • Gensymbol (bool) – Whether input gene identifiers are gene symbols.

  • species (str) – Species used for marker-database lookup.

  • weight (int) – SCSA weighting parameter.

  • tissue (str) – Tissue filter used for database matching.

  • target (str) – Marker database target (for example cellmarker).

  • celltype (str) – Cell-type mode used by SCSA.

  • norefdb (bool) – Whether to disable reference database.

  • cellrange (str or None) – Optional lineage restriction (for example T-cell subtypes only).

  • noprint (bool) – Whether to suppress verbose output.

  • list_tissue (bool) – Whether to list available tissues.

  • tissuename (str or None) – Compatibility alias for tissue.

  • speciename (str or None) – Compatibility alias for species.

Methods

__init__(adata[, foldchange, pvalue, ...])

Initialize SCSA annotation workflow configuration.

cell_anno([clustertype, cluster, rank_rep])

Annotate cell type for each cluster.

cell_anno_print()

Print the annotation result.

cell_auto_anno(adata[, clustertype, key])

Add cell type annotation to anndata.obs['scsa_celltype'].

get_celltype_marker(adata[, clustertype, ...])

Get marker genes for each clusters.

get_model_tissue([species])

List all available tissues in the database.
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