tsne
Visualize single-cell RNA sequencing data by reducing high-dimensional information into 2D or 3D plots for cluster analysis and pattern discovery.
Instructions
t-distributed stochastic neighborhood embedding (t-SNE), for visualizating single-cell data
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| n_pcs | No | Number of PCs to use. If None, automatically determined. | |
| use_rep | No | Key for .obsm to use as representation. | |
| perplexity | No | Related to number of nearest neighbors, typically between 5-50. | |
| early_exaggeration | No | Controls space between natural clusters in embedded space. | |
| learning_rate | No | Learning rate for optimization, typically between 100-1000. | |
| random_state | No | Random seed for reproducibility. | |
| use_fast_tsne | No | Whether to use Multicore-tSNE implementation. | |
| n_jobs | No | Number of jobs for parallel computation. | |
| metric | No | Distance metric to use. | euclidean |
Implementation Reference
- src/scmcp/schema/tl.py:8-67 (schema)Pydantic model defining the input schema and validation for the tsne tool.class TSNEModel(JSONParsingModel): """Input schema for the t-SNE dimensionality reduction tool.""" n_pcs: Optional[int] = Field( default=None, description="Number of PCs to use. If None, automatically determined.", ge=0 ) use_rep: Optional[str] = Field( default=None, description="Key for .obsm to use as representation." ) perplexity: Union[float, int] = Field( default=30, description="Related to number of nearest neighbors, typically between 5-50.", gt=0 ) early_exaggeration: Union[float, int] = Field( default=12, description="Controls space between natural clusters in embedded space.", gt=0 ) learning_rate: Union[float, int] = Field( default=1000, description="Learning rate for optimization, typically between 100-1000.", gt=0 ) random_state: int = Field( default=0, description="Random seed for reproducibility." ) use_fast_tsne: bool = Field( default=False, description="Whether to use Multicore-tSNE implementation." ) n_jobs: Optional[int] = Field( default=None, description="Number of jobs for parallel computation.", gt=0 ) metric: str = Field( default='euclidean', description="Distance metric to use." ) @field_validator('n_pcs', 'perplexity', 'early_exaggeration', 'learning_rate', 'n_jobs') def validate_positive_numbers(cls, v: Optional[Union[int, float]]) -> Optional[Union[int, float]]: """Validate positive numbers where applicable""" if v is not None and v <= 0: raise ValueError("must be a positive number") return v @field_validator('metric') def validate_metric(cls, v: str) -> str: """Validate distance metric is supported""" valid_metrics = ['euclidean', 'cosine', 'manhattan', 'l1', 'l2'] if v.lower() not in valid_metrics: raise ValueError(f"metric must be one of {valid_metrics}") return v.lower()
- src/scmcp/tool/tl.py:12-17 (registration)Definition of the MCP Tool object for 'tsne', including name, description, and schema reference.# Define t-SNE tool tsne_tool = types.Tool( name="tsne", description="t-distributed stochastic neighborhood embedding (t-SNE), for visualizating single-cell data", inputSchema=TSNEModel.model_json_schema(), )
- src/scmcp/tool/tl.py:125-142 (helper)Dictionary mapping 'tsne' tool name to the underlying scanpy function sc.tl.tsne used in execution.tl_func = { "tsne": sc.tl.tsne, "umap": sc.tl.umap, "draw_graph": sc.tl.draw_graph, "diffmap": sc.tl.diffmap, "embedding_density": sc.tl.embedding_density, "leiden": sc.tl.leiden, "louvain": sc.tl.louvain, "dendrogram": sc.tl.dendrogram, "dpt": sc.tl.dpt, "paga": sc.tl.paga, "ingest": sc.tl.ingest, "rank_genes_groups": sc.tl.rank_genes_groups, "filter_rank_genes_groups": sc.tl.filter_rank_genes_groups, "marker_gene_overlap": sc.tl.marker_gene_overlap, "score_genes": sc.tl.score_genes, "score_genes_cell_cycle": sc.tl.score_genes_cell_cycle, }
- src/scmcp/tool/tl.py:145-162 (registration)Dictionary registering the tsne_tool (and other tl tools) that is used by the MCP server for tool listing and dispatch.tl_tools = { "tsne": tsne_tool, "umap": umap_tool, "draw_graph": draw_graph_tool, "diffmap": diffmap_tool, "embedding_density": embedding_density_tool, "leiden": leiden_tool, "louvain": louvain_tool, "dendrogram": dendrogram_tool, "dpt": dpt_tool, "paga": paga_tool, "ingest": ingest_tool, "rank_genes_groups": rank_genes_groups_tool, "filter_rank_genes_groups": filter_rank_genes_groups_tool, "marker_gene_overlap": marker_gene_overlap_tool, "score_genes": score_genes_tool, "score_genes_cell_cycle": score_genes_cell_cycle_tool, }
- src/scmcp/tool/tl.py:164-177 (handler)Handler function that executes tl tools like tsne by calling the mapped scanpy function with validated arguments and logging.def run_tl_func(ads, func, arguments): adata = ads.adata_dic[ads.active] if func not in tl_func: raise ValueError(f"Unsupported function: {func}") run_func = tl_func[func] parameters = inspect.signature(run_func).parameters 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 Exception as e: logger.error(f"Error running function {func}: {e}") raise return