scale
Standardize single-cell RNA sequencing data by scaling to unit variance and zero mean for preprocessing and analysis.
Instructions
Scale data to unit variance and zero mean
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| zero_center | No | If False, omit zero-centering variables to handle sparse input efficiently. | |
| max_value | No | Clip (truncate) to this value after scaling. If None, do not clip. | |
| layer | No | If provided, which element of layers to scale. | |
| obsm | No | If provided, which element of obsm to scale. | |
| mask_obs | No | Boolean mask or string referring to obs column for subsetting observations. |
Implementation Reference
- src/scmcp/tool/pp.py:120-137 (handler)Generic handler that executes sc.pp.scale (via pp_func['scale']) on the active AnnData object for the 'scale' tool.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:327-361 (schema)Pydantic input schema model for the 'scale' tool, defining parameters and validation.class ScaleModel(JSONParsingModel): """Input schema for the scale preprocessing tool.""" zero_center: bool = Field( default=True, description="If False, omit zero-centering variables to handle sparse input efficiently." ) max_value: Optional[float] = Field( default=None, description="Clip (truncate) to this value after scaling. If None, do not clip." ) layer: Optional[str] = Field( default=None, description="If provided, which element of layers to scale." ) obsm: Optional[str] = Field( default=None, description="If provided, which element of obsm to scale." ) mask_obs: Optional[Union[str, bool]] = Field( default=None, description="Boolean mask or string referring to obs column for subsetting observations." ) @field_validator('max_value') def validate_max_value(cls, v: Optional[float]) -> Optional[float]: """Validate max_value is positive if provided""" if v is not None and v <= 0: raise ValueError("max_value must be positive if provided") return v
- src/scmcp/tool/pp.py:63-67 (registration)MCP tool registration defining the 'scale' tool name, description, and input schema.scale = types.Tool( name="scale", description="Scale data to unit variance and zero mean", inputSchema=ScaleModel.model_json_schema(), )
- src/scmcp/tool/pp.py:88-101 (helper)Dictionary mapping tool names to Scanpy functions, with 'scale' mapped to sc.pp.scale used by the handler.pp_func = { "filter_genes": sc.pp.filter_genes, "filter_cells": sc.pp.filter_cells, "calculate_qc_metrics": partial(sc.pp.calculate_qc_metrics, inplace=True), "log1p": sc.pp.log1p, "normalize_total": sc.pp.normalize_total, "pca": sc.pp.pca, "highly_variable_genes": sc.pp.highly_variable_genes, "regress_out": sc.pp.regress_out, "scale": sc.pp.scale, "combat": sc.pp.combat, "scrublet": sc.pp.scrublet, "neighbors": sc.pp.neighbors, }
- src/scmcp/tool/pp.py:104-117 (helper)Dictionary of registered MCP tools, including the 'scale' tool instance.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, }