ccc_rank_aggregate
Aggregates ligand-receptor interaction scores from multiple cell-cell communication methods to identify significant signaling pathways between cell groups in single-cell RNA sequencing data.
Instructions
Get an aggregate of ligand-receptor scores from multiple Cell-cell communication methods.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| groupby | Yes | Key to be used for grouping or clustering cells (e.g., cell type annotations). | |
| resource_name | No | Name of the resource to be used for ligand-receptor inference. See `li.rs.show_resources()` for available resources. | consensus |
| expr_prop | No | Minimum expression proportion for the ligands and receptors in the corresponding cell identities. Set to 0 to return unfiltered results. | |
| min_cells | No | Minimum cells per cell identity to be considered for downstream analysis. | |
| base | No | Exponent base used to reverse the log-transformation of the matrix. Relevant only for the `logfc` method. | |
| aggregate_method | No | Method aggregation approach: 'mean' for mean rank, 'rra' for RobustRankAggregate. | rra |
| return_all_lrs | No | Whether to return all ligand-receptor pairs, or only those that surpass the expr_prop threshold. | |
| key_added | No | Key under which the results will be stored in adata.uns. | liana_res |
| use_raw | No | Use raw attribute of adata if present. | |
| layer | No | Layer in AnnData.layers to use. If None, use AnnData.X. | |
| de_method | No | Differential expression method used to rank genes according to 1vsRest. | t-test |
| n_perms | No | Number of permutations for permutation-based methods. If None, no permutation testing is performed. | |
| n_jobs | No | Number of jobs to run in parallel. |
Implementation Reference
- src/scmcp/tool/ccc.py:109-141 (handler)Handler function run_ccc_func that executes ccc_rank_aggregate by dispatching to li.mt.rank_aggregate(adata, **filtered_kwargs) in the 'else' branch.def run_ccc_func(ads, func, arguments): if func not in ccc_func: raise ValueError(f"不支持的函数: {func}") run_func = ccc_func[func] adata = ads.adata_dic[ads.active] try: logger.info(f"Running function {func} with arguments {arguments}") if func == "ls_ccc_method": res = run_func() elif func == "ccc": # Extract method from arguments and pass remaining args method = arguments.get("method", "cellphonedb") method_args = {k: v for k, v in arguments.items() if k != "method"} res = run_func(adata, method, **method_args) elif "plot" in func: from ..util import savefig ax = run_func(adata, **arguments) fig_path = Path(os.getcwd()) / f"figures/{func}.png" res = savefig(ax, fig_path, format="png") add_op_log(adata, run_func, arguments) # else: parameters = inspect.signature(run_func).parameters kwargs = {k: arguments.get(k) for k in parameters if k in arguments} res = run_func(adata, **kwargs) add_op_log(adata, run_func, kwargs) return res except Exception as e: logger.error(f"Error running function {func}: {e}") raise e
- src/scmcp/schema/ccc.py:11-73 (schema)Pydantic model defining the input schema for the ccc_rank_aggregate tool, used via model_json_schema().class RankAggregateModel(JSONParsingModel): """Input schema for LIANA's rank_aggregate method for cell-cell communication analysis.""" groupby: str = Field( ..., # Required field description="Key to be used for grouping or clustering cells (e.g., cell type annotations)." ) resource_name: str = Field( default="consensus", description="Name of the resource to be used for ligand-receptor inference. See `li.rs.show_resources()` for available resources." ) expr_prop: float = Field( default=0.1, description="Minimum expression proportion for the ligands and receptors in the corresponding cell identities. Set to 0 to return unfiltered results." ) min_cells: int = Field( default=5, description="Minimum cells per cell identity to be considered for downstream analysis." ) base: float = Field( default=2.718281828459045, # e description="Exponent base used to reverse the log-transformation of the matrix. Relevant only for the `logfc` method." ) aggregate_method: Literal["rra", "mean"] = Field( default="rra", description="Method aggregation approach: 'mean' for mean rank, 'rra' for RobustRankAggregate." ) return_all_lrs: bool = Field( default=False, description="Whether to return all ligand-receptor pairs, or only those that surpass the expr_prop threshold." ) key_added: str = Field( default="liana_res", description="Key under which the results will be stored in adata.uns." ) use_raw: Optional[bool] = Field( default=True, description="Use raw attribute of adata if present." ) layer: Optional[str] = Field( default=None, description="Layer in AnnData.layers to use. If None, use AnnData.X." ) de_method: str = Field( default="t-test", description="Differential expression method used to rank genes according to 1vsRest." ) n_perms: int = Field( default=1000, description="Number of permutations for permutation-based methods. If None, no permutation testing is performed." )
- src/scmcp/tool/ccc.py:22-26 (registration)Creates the MCP Tool object for ccc_rank_aggregate with name, description, and input schema.rank_aggregate_tool = types.Tool( name="ccc_rank_aggregate", description="Get an aggregate of ligand-receptor scores from multiple Cell-cell communication methods. ", inputSchema=RankAggregateModel.model_json_schema(), )
- src/scmcp/tool/ccc.py:92-98 (helper)Maps tool name 'ccc_rank_aggregate' to the actual function li.mt.rank_aggregate for execution.ccc_func = { "ls_ccc_method": ls_ccc_method, "ccc_rank_aggregate": li.mt.rank_aggregate, "ccc_circle_plot": plot_circleplot, "ccc_dot_plot": plot_dotplot, "ccc": run_ccc, }
- src/scmcp/tool/ccc.py:100-106 (registration)Registers the rank_aggregate_tool in the ccc_tools dictionary, which is used by server.list_tools() and call_tool() dispatch.ccc_tools = { "ls_ccc_method": ls_ccc_method_tool, "ccc_rank_aggregate": rank_aggregate_tool, "ccc_circle_plot": circle_plot_tool, "ccc_dot_plot": dot_plot_tool, "ccc": ccc_tool, }