calculate_qc_metrics

Instructions

Implementation Reference

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

  Handler function that executes the calculate_qc_metrics tool logic by calling the mapped scanpy.pp.calculate_qc_metrics function on the active AnnData object with provided arguments and inplace=True.

  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:76-124 (schema)

  Pydantic input schema model for the calculate_qc_metrics tool, including fields like qc_vars, percent_top, layer, use_raw, log1p with validation.

  class CalculateQCMetrics(JSONParsingModel):
      """Input schema for the calculate_qc_metrics preprocessing tool."""
      
      expr_type: str = Field(
          default="counts",
          description="Name of kind of values in X."
      )
      
      var_type: str = Field(
          default="genes",
          description="The kind of thing the variables are."
      )
      
      qc_vars: Optional[Union[List[str], str]]  = Field(
          default=[],
          description=(
              "Keys for boolean columns of .var which identify variables you could want to control for "
              "mark_var tool should be called frist when you want to calculate mt, ribo, hb, and check tool output for var columns"
          )
      )
      
      percent_top: Optional[List[int]] = Field(
          default=[50, 100, 200, 500],
          description="List of ranks (where genes are ranked by expression) at which the cumulative proportion of expression will be reported as a percentage."
      )
      
      layer: Optional[str] = Field(
          default=None,
          description="If provided, use adata.layers[layer] for expression values instead of adata.X"
      )
      
      use_raw: bool = Field(
          default=False,
          description="If True, use adata.raw.X for expression values instead of adata.X"
      )
      log1p: bool = Field(
          default=True,
          description="Set to False to skip computing log1p transformed annotations."
      )
      
      @field_validator('percent_top')
      def validate_percent_top(cls, v: Optional[List[int]]) -> Optional[List[int]]:
          """验证 percent_top 中的值为正整数"""
          if v is not None:
              for rank in v:
                  if not isinstance(rank, int) or rank <= 0:
                      raise ValueError("percent_top 中的所有值必须是正整数")
          return v

• src/scmcp/tool/pp.py:27-31 (registration)

  MCP Tool object registration defining the name, description, and input schema for calculate_qc_metrics.

  calculate_qc_metrics = types.Tool(
      name="calculate_qc_metrics",
      description="Calculate quality control metrics(common metrics: total counts, gene number, percentage of counts in ribosomal and mitochondrial) for AnnData.",
      inputSchema=CalculateQCMetrics.model_json_schema(),
  )

• src/scmcp/tool/pp.py:91-91 (helper)

  pp_func dictionary entry mapping the tool name to the wrapped scanpy function partial(sc.pp.calculate_qc_metrics, inplace=True).

  "calculate_qc_metrics": partial(sc.pp.calculate_qc_metrics, inplace=True),

• src/scmcp/tool/pp.py:104-117 (registration)

  pp_tools dictionary that includes the calculate_qc_metrics tool, used by server.list_tools() to expose the tool.

  pp_tools = {
      "filter_genes": filter_genes,
      "filter_cells": filter_cells,
      "calculate_qc_metrics": calculate_qc_metrics,
      "log1p": log1p,
      "normalize_total": normalize_total,
      "pca": pca,
      "highly_variable_genes": highly_variable_genes,
      "regress_out": regress_out,
      "scale": scale,
      "combat": combat,
      "scrublet": scrublet,
      "neighbors": neighbors,
  }