File size: 3,074 Bytes
e918a5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Data models for Pathway Analysis Environment.
Supports pipeline-style episodes (count matrix + sample metadata + gene sets)
with PyDESeq2 differential expression, Fisher ORA, overlap-aware summaries,
and HTML step traces.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from openenv.core.env_server import Action, Observation, State
from pydantic import Field
class PathwayAction(Action):
"""
Action for the Pathway Analysis environment.
action_type:
- ``inspect_dataset``: describe available samples and conditions.
- ``understand_experiment_design``: **(1)** Summarize groups (conditions, sample counts);
optionally **(2)** validate ``condition_a``/``condition_b`` as reference/alternate for
DGE (does not run DESeq2). Valid pairs feed ``run_differential_expression`` when DE omits
conditions. **(3)** Pathway steps follow DE.
- ``run_differential_expression``: PyDESeq2 contrast (needs ``condition_a`` /
``condition_b`` when using count-matrix cases).
- ``run_pathway_enrichment``: ORA on DE genes vs pathway gene sets.
- ``compare_pathways``: contrast exclusive vs shared DE support between two
pathways (``pathway_a``, ``pathway_b``).
- ``submit_answer``: submit ``hypothesis`` pathway name and end episode.
"""
action_type: str
condition_a: Optional[str] = None
condition_b: Optional[str] = None
gene_list: Optional[List[str]] = None
hypothesis: Optional[str] = None
pathway_a: Optional[str] = None
pathway_b: Optional[str] = None
class PathwayObservation(Observation):
"""Observation with optional rich DE / ORA structures (JSON-serializable)."""
message: str = ""
available_conditions: List[str] = Field(default_factory=list)
top_genes: List[str] = Field(default_factory=list)
top_pathways: List[str] = Field(default_factory=list)
de_genes: List[Dict[str, Any]] = Field(default_factory=list)
pathway_enrichment: List[Dict[str, Any]] = Field(default_factory=list)
pathway_comparison: Optional[Dict[str, Any]] = None
overlap_summary: Optional[Dict[str, Any]] = None
statistical_ambiguity: Optional[Dict[str, Any]] = None
trace_path: Optional[str] = None
experiment_design: Optional[Dict[str, Any]] = None
class PathwayState(State):
"""Agent-visible episode state (ground truth is never included)."""
conditions: List[str] = Field(default_factory=list)
de_run: bool = False
enrichment_run: bool = False
is_done: bool = False
pipeline_mode: bool = False
strict_mode: bool = False
legacy_mode: bool = False
eval_mode: bool = True
max_steps: int = 30
design_understood: bool = False
validated_reference: Optional[str] = None
validated_alternate: Optional[str] = None
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