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Each ``Scenario`` bundles a task specification together with the matching
hidden ground-truth biology so the simulator can instantiate consistent
episodes. The library is intentionally diverse: it covers differential
expression, trajectory inference, perturbation response, and biomarker
validation across tissues and modalities.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from models import ExpectedFinding, PaperReference, TaskSpec
from server.simulator.latent_state import (
CellPopulation,
GeneProgram,
LatentBiologicalState,
TechnicalState,
)
@dataclass
class Scenario:
"""A reproducible (task, ground-truth) pair."""
name: str
task: TaskSpec
biology: LatentBiologicalState
technical: TechnicalState = field(default_factory=TechnicalState)
hidden_failure_conditions: List[str] = field(default_factory=list)
difficulty: str = "medium"
tags: List[str] = field(default_factory=list)
# ── Scenario library ────────────────────────────────────────────────────────
SCENARIO_LIBRARY: List[Scenario] = [
# ── 1. Cardiac disease DE ───────────────────────────────────────────
Scenario(
name="cardiac_disease_de",
difficulty="easy",
tags=["de", "scRNA-seq", "cardiac"],
task=TaskSpec(
problem_statement=(
"Identify differentially expressed genes between diseased "
"and healthy cardiomyocytes using single-cell RNA sequencing."
),
modality="scRNA-seq",
organism="human",
tissue="heart",
conditions=["healthy", "dilated_cardiomyopathy"],
budget_limit=80_000.0,
time_limit_days=120.0,
success_criteria=[
"Identify DE genes between conditions",
"Validate at least one candidate marker",
],
),
biology=LatentBiologicalState(
cell_populations=[
CellPopulation(
name="cardiomyocyte",
proportion=0.35,
marker_genes=["TNNT2", "MYH7", "ACTC1"],
state="contractile",
condition_response={"dilated_cardiomyopathy": 0.8},
),
CellPopulation(
name="fibroblast",
proportion=0.25,
marker_genes=["COL1A1", "DCN", "LUM"],
state="quiescent",
condition_response={"dilated_cardiomyopathy": 1.3},
),
CellPopulation(
name="endothelial",
proportion=0.15,
marker_genes=["PECAM1", "VWF", "CDH5"],
state="quiescent",
),
CellPopulation(
name="macrophage",
proportion=0.10,
marker_genes=["CD68", "CD163", "CSF1R"],
state="activated",
condition_response={"dilated_cardiomyopathy": 1.5},
),
CellPopulation(
name="smooth_muscle",
proportion=0.15,
marker_genes=["ACTA2", "MYH11", "TAGLN"],
state="quiescent",
),
],
true_de_genes={
"disease_vs_healthy": {
"NPPA": 2.5, "NPPB": 3.1, "MYH7": 1.8,
"COL1A1": 1.6, "COL3A1": 1.4, "POSTN": 2.0,
"CCL2": 1.2, "IL6": 0.9, "TGFB1": 1.1,
"ANKRD1": 2.2, "XIRP2": -1.3, "MYL2": -0.8,
},
},
true_pathways={
"cardiac_muscle_contraction": 0.4,
"extracellular_matrix_organisation": 0.85,
"inflammatory_response": 0.7,
"TGF_beta_signalling": 0.75,
"apoptosis": 0.55,
},
true_markers=["NPPA", "NPPB", "POSTN", "COL1A1"],
causal_mechanisms=[
"TGF-beta-driven fibrosis",
"inflammatory macrophage infiltration",
],
n_true_cells=12_000,
),
technical=TechnicalState(
batch_effects={"batch_1": 0.15, "batch_2": 0.10},
doublet_rate=0.05,
dropout_rate=0.08,
),
),
# ── 2. Developmental trajectory ─────────────────────────────────────
Scenario(
name="hematopoiesis_trajectory",
difficulty="medium",
tags=["trajectory", "scRNA-seq", "hematopoiesis"],
task=TaskSpec(
problem_statement=(
"Infer the developmental trajectory of hematopoietic "
"stem cells differentiating into mature blood lineages."
),
modality="scRNA-seq",
organism="human",
tissue="bone_marrow",
conditions=["steady_state"],
budget_limit=100_000.0,
time_limit_days=150.0,
success_criteria=[
"Reconstruct branching lineage structure",
"Identify key transcription factors driving fate decisions",
],
paper_references=[
PaperReference(
title=(
"Single-cell RNA-sequencing uncovers transcriptional "
"states and fate decisions in haematopoiesis"
),
citation="Nature Communications (2018)",
doi="10.1038/s41467-017-02305-6",
url=(
"https://www.nature.com/articles/"
"s41467-017-02305-6"
),
),
],
expected_findings=[
ExpectedFinding(
finding=(
"Trajectory analysis should recover branching blood "
"lineages rooted in HSCs."
),
category="trajectory",
keywords=["HSC", "branching", "lineage", "trajectory"],
),
ExpectedFinding(
finding=(
"GATA1 should appear as a driver of erythroid fate "
"commitment."
),
category="regulatory_network",
keywords=["GATA1", "erythroid", "commitment"],
),
ExpectedFinding(
finding=(
"CEBPA and SPI1 should support myeloid branch "
"decisions."
),
category="regulatory_network",
keywords=["CEBPA", "SPI1", "myeloid", "branch"],
),
],
),
biology=LatentBiologicalState(
cell_populations=[
CellPopulation(name="HSC", proportion=0.05,
marker_genes=["CD34", "KIT", "THY1"],
state="stem"),
CellPopulation(name="CMP", proportion=0.10,
marker_genes=["CD34", "FLT3"],
state="progenitor"),
CellPopulation(name="GMP", proportion=0.12,
marker_genes=["CSF3R", "CEBPA"],
state="progenitor"),
CellPopulation(name="MEP", proportion=0.10,
marker_genes=["GATA1", "KLF1"],
state="progenitor"),
CellPopulation(name="erythrocyte", proportion=0.20,
marker_genes=["HBA1", "HBB", "GYPA"],
state="mature"),
CellPopulation(name="neutrophil", proportion=0.18,
marker_genes=["ELANE", "MPO", "CTSG"],
state="mature"),
CellPopulation(name="monocyte", proportion=0.15,
marker_genes=["CD14", "CSF1R", "FCGR3A"],
state="mature"),
CellPopulation(name="megakaryocyte", proportion=0.10,
marker_genes=["ITGA2B", "GP1BA"],
state="mature"),
],
true_de_genes={},
true_pathways={
"hematopoietic_cell_lineage": 0.9,
"MAPK_signalling": 0.6,
"JAK_STAT_signalling": 0.7,
},
true_trajectory={
"root": "HSC",
"n_lineages": 3,
"branching": True,
"branches": [
["HSC", "CMP", "GMP", "neutrophil"],
["HSC", "CMP", "GMP", "monocyte"],
["HSC", "MEP", "erythrocyte"],
["HSC", "MEP", "megakaryocyte"],
],
},
true_regulatory_network={
"GATA1": ["KLF1", "HBB", "HBA1", "GYPA"],
"CEBPA": ["CSF3R", "ELANE", "MPO"],
"SPI1": ["CSF1R", "CD14", "FCGR3A"],
"RUNX1": ["CD34", "KIT"],
},
true_markers=["GATA1", "CEBPA", "SPI1"],
causal_mechanisms=[
"GATA1-driven erythroid commitment",
"PU.1/CEBPA antagonism at myeloid branch point",
],
n_true_cells=15_000,
),
technical=TechnicalState(dropout_rate=0.12, doublet_rate=0.06),
),
# ── 3. Perturbation response ────────────────────────────────────────
Scenario(
name="perturbation_immune",
difficulty="hard",
tags=["perturbation", "scRNA-seq", "immune"],
task=TaskSpec(
problem_statement=(
"Determine the effect of JAK inhibitor treatment on "
"T-cell activation states in rheumatoid arthritis."
),
modality="scRNA-seq",
organism="human",
tissue="synovial_fluid",
conditions=["untreated_RA", "JAK_inhibitor_treated"],
budget_limit=120_000.0,
time_limit_days=180.0,
prior_observations=[
"Elevated JAK-STAT signalling observed in prior bulk RNA-seq",
],
success_criteria=[
"Quantify shift in T-cell activation states",
"Identify pathways modulated by JAK inhibitor",
"Propose validation strategy",
],
),
biology=LatentBiologicalState(
cell_populations=[
CellPopulation(name="CD4_Th1", proportion=0.20,
marker_genes=["IFNG", "TBX21", "IL2"],
state="activated",
condition_response={"JAK_inhibitor_treated": 0.5}),
CellPopulation(name="CD4_Th17", proportion=0.15,
marker_genes=["IL17A", "RORC", "CCR6"],
state="activated",
condition_response={"JAK_inhibitor_treated": 0.6}),
CellPopulation(name="CD4_Treg", proportion=0.08,
marker_genes=["FOXP3", "IL2RA", "CTLA4"],
state="regulatory",
condition_response={"JAK_inhibitor_treated": 1.2}),
CellPopulation(name="CD8_cytotoxic", proportion=0.18,
marker_genes=["GZMB", "PRF1", "CD8A"],
state="activated",
condition_response={"JAK_inhibitor_treated": 0.7}),
CellPopulation(name="macrophage", proportion=0.15,
marker_genes=["CD68", "CD163", "MARCO"],
state="inflammatory"),
CellPopulation(name="fibroblast", proportion=0.14,
marker_genes=["COL1A1", "FAP", "THY1"],
state="activated"),
CellPopulation(name="B_cell", proportion=0.10,
marker_genes=["CD19", "MS4A1", "CD79A"],
state="quiescent"),
],
true_de_genes={
"treated_vs_untreated": {
"IFNG": -1.8, "TBX21": -1.2, "IL17A": -1.5,
"RORC": -0.9, "JAK1": -0.3, "STAT1": -1.0,
"STAT3": -0.8, "SOCS1": 1.5, "SOCS3": 1.3,
"FOXP3": 0.6, "IL10": 0.7,
},
},
true_pathways={
"JAK_STAT_signalling": 0.3,
"Th1_differentiation": 0.35,
"Th17_differentiation": 0.4,
"cytokine_signalling": 0.45,
"regulatory_T_cell_function": 0.7,
},
perturbation_effects={
"JAK_inhibitor": {
"STAT1": -0.8, "STAT3": -0.7, "IFNG": -1.5,
"IL17A": -1.3, "SOCS1": 1.2,
},
},
true_markers=["STAT1", "SOCS1", "IFNG"],
causal_mechanisms=[
"JAK-STAT pathway inhibition reduces Th1/Th17 activation",
"Compensatory Treg expansion under JAK inhibition",
],
n_true_cells=18_000,
),
technical=TechnicalState(
batch_effects={"batch_ctrl": 0.12, "batch_treated": 0.18},
ambient_rna_fraction=0.07,
dropout_rate=0.10,
),
hidden_failure_conditions=[
"High ambient RNA may confound DE in low-abundance transcripts",
],
),
# ── 4. Biomarker validation ─────────────────────────────────────────
Scenario(
name="biomarker_validation_lung",
difficulty="medium",
tags=["biomarker", "validation", "scRNA-seq", "lung"],
task=TaskSpec(
problem_statement=(
"Design a follow-up validation experiment for candidate "
"biomarker SPP1 in idiopathic pulmonary fibrosis (IPF)."
),
modality="scRNA-seq",
organism="human",
tissue="lung",
conditions=["healthy", "IPF"],
budget_limit=90_000.0,
time_limit_days=150.0,
prior_observations=[
"A macrophage subpopulation shows elevated expression in IPF tissue relative to controls",
"Pro-fibrotic macrophage enrichment has been observed in fibrotic regions by spatial profiling",
],
success_criteria=[
"Validate SPP1 as a marker for pro-fibrotic macrophages",
"Confirm spatial localisation in fibrotic tissue",
],
paper_references=[
PaperReference(
title=(
"Proliferating SPP1/MERTK-expressing macrophages in "
"idiopathic pulmonary fibrosis"
),
citation="European Respiratory Journal (2019)",
doi="10.1183/13993003.02441-2018",
pmid="31221805",
url="https://pubmed.ncbi.nlm.nih.gov/31221805/",
),
],
expected_findings=[
ExpectedFinding(
finding=(
"SPP1-positive macrophages should be enriched in IPF "
"fibrotic regions."
),
category="marker",
keywords=["SPP1", "macrophage", "IPF", "fibrotic"],
),
ExpectedFinding(
finding=(
"MERTK should co-occur with the profibrotic macrophage "
"state."
),
category="marker",
keywords=["MERTK", "macrophage", "SPP1"],
),
ExpectedFinding(
finding=(
"Extracellular matrix organization should emerge as a "
"top fibrotic program."
),
category="pathway",
keywords=["extracellular_matrix", "fibrosis", "pathway"],
),
],
dataset_metadata={
"literature_grounding": "single_cell_ipf_macrophages",
},
),
biology=LatentBiologicalState(
cell_populations=[
CellPopulation(name="alveolar_macrophage", proportion=0.18,
marker_genes=["MARCO", "FABP4", "MCEMP1"],
state="resident"),
CellPopulation(name="SPP1_macrophage", proportion=0.12,
marker_genes=["SPP1", "MERTK", "MMP9", "TREM2"],
state="pro-fibrotic",
condition_response={"IPF": 2.0}),
CellPopulation(name="AT2", proportion=0.20,
marker_genes=["SFTPC", "SFTPB", "ABCA3"],
state="normal"),
CellPopulation(name="fibroblast", proportion=0.22,
marker_genes=["COL1A1", "COL3A1", "POSTN"],
state="activated",
condition_response={"IPF": 1.5}),
CellPopulation(name="endothelial", proportion=0.13,
marker_genes=["PECAM1", "CLDN5"],
state="quiescent"),
CellPopulation(name="T_cell", proportion=0.15,
marker_genes=["CD3D", "CD3E", "IL7R"],
state="quiescent"),
],
true_de_genes={
"IPF_vs_healthy": {
"SPP1": 3.2, "MERTK": 1.4, "MMP9": 1.8, "TREM2": 1.5,
"COL1A1": 2.1, "COL3A1": 1.9, "POSTN": 2.4,
"SFTPC": -1.2, "AGER": -1.6,
},
},
true_pathways={
"extracellular_matrix_organisation": 0.9,
"integrin_signalling": 0.75,
"macrophage_activation": 0.8,
"Wnt_signalling": 0.6,
},
true_markers=["SPP1", "MERTK", "POSTN", "MMP9"],
causal_mechanisms=[
"SPP1+ macrophage-driven fibroblast activation",
"Integrin-mediated SPP1 signalling in fibrosis",
],
n_true_cells=14_000,
),
technical=TechnicalState(
batch_effects={"batch_1": 0.10},
dropout_rate=0.09,
sample_quality=0.85,
),
),
]
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