SCMCP

Instructions

Implementation Reference

• src/scmcp/tool/pp.py:120-137 (handler)

  Handler function that executes the highly_variable_genes tool by dispatching to sc.pp.highly_variable_genes on the AnnData object with validated arguments.
  def run_pp_func(ads, func, arguments): adata = ads.adata_dic[ads.active] if func not in pp_func: raise ValueError(f"不支持的函数: {func}") run_func = pp_func[func] parameters = inspect.signature(run_func).parameters arguments["inplace"] = True kwargs = {k: arguments.get(k) for k in parameters if k in arguments} try: res = run_func(adata, **kwargs) add_op_log(adata, run_func, kwargs) except KeyError as e: raise KeyError(f"Can not foud {e} column in adata.obs or adata.var") except Exception as e: raise e return res
• src/scmcp/schema/pp.py:220-292 (schema)

  Pydantic input schema model with fields and validators for the highly_variable_genes tool.
  class HighlyVariableGenesModel(JSONParsingModel): """Input schema for the highly_variable_genes preprocessing tool.""" layer: Optional[str] = Field( default=None, description="If provided, use adata.layers[layer] for expression values." ) n_top_genes: Optional[int] = Field( default=None, description="Number of highly-variable genes to keep. Mandatory if `flavor='seurat_v3'", ) min_disp: float = Field( default=0.5, description="Minimum dispersion cutoff for gene selection." ) max_disp: float = Field( default=float('inf'), description="Maximum dispersion cutoff for gene selection." ) min_mean: float = Field( default=0.0125, description="Minimum mean expression cutoff for gene selection." ) max_mean: float = Field( default=3, description="Maximum mean expression cutoff for gene selection." ) span: float = Field( default=0.3, description="Fraction of data used for loess model fit in seurat_v3.", gt=0, lt=1 ) n_bins: int = Field( default=20, description="Number of bins for mean expression binning.", gt=0 ) flavor: Literal['seurat', 'cell_ranger', 'seurat_v3', 'seurat_v3_paper'] = Field( default='seurat', description="Method for identifying highly variable genes." ) subset: bool = Field( default=False, description="Inplace subset to highly-variable genes if True." ) batch_key: Optional[str] = Field( default=None, description="Key in adata.obs for batch information." ) check_values: bool = Field( default=True, description="Check if counts are integers for seurat_v3 flavor." ) @field_validator('n_top_genes', 'n_bins') def validate_positive_integers(cls, v: Optional[int]) -> Optional[int]: """Validate positive integers""" if v is not None and v <= 0: raise ValueError("must be a positive integer") return v @field_validator('span') def validate_span(cls, v: float) -> float: """Validate span is between 0 and 1""" if v <= 0 or v >= 1: raise ValueError("span must be between 0 and 1") return v
• src/scmcp/tool/pp.py:51-55 (registration)

  MCP tool registration defining the highly_variable_genes tool with its schema.
  highly_variable_genes = types.Tool( name="highly_variable_genes", description="Annotate highly variable genes", inputSchema=HighlyVariableGenesModel.model_json_schema(), )
• src/scmcp/tool/pp.py:95-95 (helper)

  Mapping from tool name to the underlying Scanpy function sc.pp.highly_variable_genes.
  "highly_variable_genes": sc.pp.highly_variable_genes,
• src/scmcp/tool/pp.py:111-111 (helper)

  Addition of the highly_variable_genes tool to the pp_tools dictionary for export.
  "highly_variable_genes": highly_variable_genes,