"""
Tools schema definitions for infercnvpy-mcp.
"""
from pydantic import (
Field,
ValidationInfo,
computed_field,
field_validator,
model_validator,
BaseModel
)
from typing import Optional, Union, List, Dict, Any, Literal, Sequence
class InferCNVParam(BaseModel):
"""Input schema for the infercnv tool."""
reference_key: Optional[str] = Field(
default=None,
description="Column name in adata.obs that contains tumor/normal cell cluster annotations."
)
reference_cat: Optional[List[str]] = Field(
default=None,
description="One or multiple values in adata.obs[reference_key] that annotate normal cells."
)
# reference: Optional[Any] = Field(
# default=None,
# description="Directly supply an array of average normal gene expression. Overrides reference_key and reference_cat."
# )
lfc_clip: float = Field(
default=3.0,
description="Clip log fold changes at this value"
)
window_size: int = Field(
default=100,
description="Size of the running window (number of genes to include in the window)"
)
step: int = Field(
default=10,
description="Only compute every nth running window where n = step. Set to 1 to compute all windows."
)
dynamic_threshold: Optional[float] = Field(
default=1.5,
description="Values < dynamic threshold * STDDEV will be set to 0, where STDDEV is the standard deviation of the smoothed gene expression. Set to None to disable this step."
)
exclude_chromosomes: Optional[Sequence[str]] = Field(
default=('chrX', 'chrY'),
description="List of chromosomes to exclude. The default is to exclude genosomes."
)
chunksize: int = Field(
default=5000,
description="Process dataset in chunks of cells. This allows to run infercnv on datasets with many cells, where the dense matrix would not fit into memory."
)
n_jobs: Optional[int] = Field(
default=None,
description="Number of jobs for parallel processing. Default: use all cores. Data will be submitted to workers in chunks, see chunksize."
)
layer: Optional[str] = Field(
default=None,
description="Layer from adata to use. If None, use X."
)
calculate_gene_values: bool = Field(
default=False,
description="If True per gene CNVs will be calculated and stored in adata.layers['gene_values_cnv']."
)
class CNVScoreParam(BaseModel):
groupby: str = Field(
default='cnv_leiden',
description="Key under which the clustering is stored in adata.obs. Usually the result of infercnvpy.tl.leiden(), but could also be other grouping information, e.g. sample or patient information."
)
key_added: str = Field(
default='cnv_score',
description="Key under which the score will be stored in adata.obs."
)
