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from __future__ import annotations
from typing import Any, Dict, List
from models import (
ActionType,
ExperimentAction,
IntermediateOutput,
OutputType,
)
from .latent_state import FullLatentState
from .noise import NoiseModel
class OutputGenerator:
"""Creates structured ``IntermediateOutput`` objects conditioned on the
hidden latent state, the action taken, and a stochastic noise model.
"""
def __init__(self, noise: NoiseModel):
self.noise = noise
def generate(
self,
action: ExperimentAction,
state: FullLatentState,
step_index: int,
) -> IntermediateOutput:
handler = _HANDLERS.get(action.action_type, self._default)
return handler(self, action, state, step_index)
# ── wet-lab outputs ─────────────────────────────────────────────────
def _collect_sample(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
n_samples = action.parameters.get("n_samples", 6)
quality = self.noise.quality_degradation(
s.technical.sample_quality, [s.technical.capture_efficiency]
)
return IntermediateOutput(
output_type=OutputType.SAMPLE_COLLECTION_RESULT,
step_index=idx,
quality_score=quality,
summary=f"Collected {n_samples} samples (quality={quality:.2f})",
data={
"n_samples": n_samples,
"quality": quality,
"organism": "human",
"tissue": "blood",
},
artifacts_available=["raw_samples"],
)
def _select_cohort(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
criteria = action.parameters.get("criteria", {})
n_selected = action.parameters.get("n_selected", 4)
return IntermediateOutput(
output_type=OutputType.COHORT_RESULT,
step_index=idx,
summary=f"Selected cohort of {n_selected} samples with criteria {criteria}",
data={"n_selected": n_selected, "criteria": criteria},
artifacts_available=["cohort_manifest"],
)
def _prepare_library(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
complexity = self.noise.quality_degradation(
s.technical.library_complexity,
[s.technical.sample_quality],
)
return IntermediateOutput(
output_type=OutputType.LIBRARY_PREP_RESULT,
step_index=idx,
quality_score=complexity,
summary=f"Library prepared (complexity={complexity:.2f})",
data={
"library_complexity": complexity,
"method": action.method or "10x_chromium",
},
artifacts_available=["prepared_library"],
)
def _culture_cells(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
days = action.parameters.get("days", 7)
viability = self.noise.sample_qc_metric(0.92, 0.05, 0.5, 1.0)
return IntermediateOutput(
output_type=OutputType.CULTURE_RESULT,
step_index=idx,
quality_score=viability,
summary=f"Cultured for {days}d, viability={viability:.2f}",
data={"days": days, "viability": viability},
artifacts_available=["cultured_cells"],
)
def _perturb(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
target = action.parameters.get("target", "unknown")
efficiency = self.noise.sample_qc_metric(0.75, 0.15, 0.0, 1.0)
return IntermediateOutput(
output_type=OutputType.PERTURBATION_RESULT,
step_index=idx,
quality_score=efficiency,
summary=f"Perturbation of {target} (efficiency={efficiency:.2f})",
data={
"target": target,
"efficiency": efficiency,
"type": action.action_type.value,
},
artifacts_available=["perturbed_cells"],
)
def _sequence_cells(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
depth = s.technical.sequencing_depth_factor
n_cells = self.noise.sample_count(
s.biology.n_true_cells * s.technical.capture_efficiency
)
n_genes = self.noise.sample_count(18_000)
median_umi = self.noise.sample_count(int(3000 * depth))
quality = self.noise.quality_degradation(
s.technical.sample_quality,
[s.technical.library_complexity, s.technical.capture_efficiency],
)
return IntermediateOutput(
output_type=OutputType.SEQUENCING_RESULT,
step_index=idx,
quality_score=quality,
summary=(
f"Sequenced {n_cells} cells, {n_genes} genes detected, "
f"median UMI={median_umi}"
),
data={
"n_cells": n_cells,
"n_genes": n_genes,
"median_umi": median_umi,
"sequencing_saturation": self.noise.sample_qc_metric(0.7, 0.1),
},
artifacts_available=["raw_count_matrix"],
)
# ── computational outputs ───────────────────────────────────────────
def _run_qc(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
doublet_frac = self.noise.sample_qc_metric(
s.technical.doublet_rate, 0.01, 0.0, 0.2
)
mito_frac = self.noise.sample_qc_metric(0.05, 0.02, 0.0, 0.3)
ambient_frac = self.noise.sample_qc_metric(
s.technical.ambient_rna_fraction, 0.01, 0.0, 0.2
)
warnings: List[str] = []
if doublet_frac > 0.08:
warnings.append(f"High doublet rate ({doublet_frac:.1%})")
if mito_frac > 0.1:
warnings.append(f"High mitochondrial fraction ({mito_frac:.1%})")
quality = 1.0 - (doublet_frac + mito_frac + ambient_frac)
return IntermediateOutput(
output_type=OutputType.QC_METRICS,
step_index=idx,
quality_score=max(0.0, quality),
summary="QC metrics computed",
data={
"doublet_fraction": doublet_frac,
"mitochondrial_fraction": mito_frac,
"ambient_rna_fraction": ambient_frac,
"median_genes_per_cell": self.noise.sample_count(2500),
"median_umi_per_cell": self.noise.sample_count(8000),
},
warnings=warnings,
artifacts_available=["qc_report"],
)
def _filter_data(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
retain_frac = self.noise.sample_qc_metric(0.85, 0.05, 0.5, 1.0)
n_before = s.biology.n_true_cells
n_after = max(100, int(n_before * retain_frac))
return IntermediateOutput(
output_type=OutputType.COUNT_MATRIX_SUMMARY,
step_index=idx,
quality_score=retain_frac,
summary=f"Filtered {n_before} → {n_after} cells ({retain_frac:.0%} retained)",
data={
"n_cells_before": n_before,
"n_cells_after": n_after,
"n_genes_retained": self.noise.sample_count(15_000),
"retain_fraction": retain_frac,
},
artifacts_available=["filtered_count_matrix"],
)
def _normalize_data(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
method = action.method or "log_normalize"
return IntermediateOutput(
output_type=OutputType.COUNT_MATRIX_SUMMARY,
step_index=idx,
summary=f"Normalized with {method}",
data={"method": method, "n_hvg": self.noise.sample_count(2000)},
artifacts_available=["normalized_matrix", "hvg_list"],
)
def _integrate_batches(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
method = action.method or "harmony"
residual = self.noise.sample_qc_metric(0.05, 0.03, 0.0, 0.3)
return IntermediateOutput(
output_type=OutputType.EMBEDDING_SUMMARY,
step_index=idx,
quality_score=1.0 - residual,
summary=f"Batch integration ({method}), residual batch effect={residual:.2f}",
data={
"method": method,
"residual_batch_effect": residual,
"n_batches": len(s.technical.batch_effects) or 1,
},
artifacts_available=["integrated_embedding"],
)
def _cluster_cells(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
n_true = len(s.biology.cell_populations) or 5
quality = self.noise.quality_degradation(0.8, [0.95])
n_clusters = self.noise.sample_cluster_count(n_true, quality)
cluster_names = [f"cluster_{i}" for i in range(n_clusters)]
sizes = self._random_partition(s.biology.n_true_cells, n_clusters)
return IntermediateOutput(
output_type=OutputType.CLUSTER_RESULT,
step_index=idx,
quality_score=quality,
summary=f"Found {n_clusters} clusters (ground-truth populations: {n_true})",
data={
"n_clusters": n_clusters,
"cluster_names": cluster_names,
"cluster_sizes": sizes,
"silhouette_score": self.noise.sample_qc_metric(0.35, 0.1, -1.0, 1.0),
},
uncertainty=abs(n_clusters - n_true) / max(n_true, 1),
artifacts_available=["cluster_assignments", "umap_embedding"],
)
def _differential_expression(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
comparison = action.parameters.get("comparison", "disease_vs_healthy")
true_effects = s.biology.true_de_genes.get(comparison, {})
n_cells = s.progress.n_cells_after_filter or s.biology.n_true_cells
noise_level = s.technical.dropout_rate + 0.1 * (1.0 - s.technical.sample_quality)
observed = self.noise.sample_effect_sizes(true_effects, n_cells, noise_level)
fp_genes = self.noise.generate_false_positives(5000, 0.002 + noise_level * 0.01)
for g in fp_genes:
observed[g] = float(self.noise.rng.normal(0, 0.3))
fn_genes = self.noise.generate_false_negatives(list(true_effects.keys()), 0.15)
for g in fn_genes:
observed.pop(g, None)
top_genes = sorted(observed.items(), key=lambda kv: abs(kv[1]), reverse=True)[:50]
return IntermediateOutput(
output_type=OutputType.DE_RESULT,
step_index=idx,
quality_score=self.noise.quality_degradation(0.8, [1.0 - noise_level]),
summary=f"DE analysis ({comparison}): {len(observed)} genes tested, {len(top_genes)} top hits",
data={
"comparison": comparison,
"n_tested": len(observed),
"top_genes": [
{"gene": g, "log2FC": round(fc, 3)} for g, fc in top_genes
],
"n_significant": sum(1 for _, fc in observed.items() if abs(fc) > 0.5),
},
uncertainty=noise_level,
artifacts_available=["de_table"],
)
def _trajectory_analysis(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
has_trajectory = s.biology.true_trajectory is not None
quality = self.noise.quality_degradation(0.7 if has_trajectory else 0.3, [0.9])
summary_data: Dict[str, Any] = {"method": action.method or "monocle3"}
if has_trajectory:
summary_data.update({
"n_lineages": s.biology.true_trajectory.get("n_lineages", 1),
"pseudotime_range": [0.0, 1.0],
"branching_detected": s.biology.true_trajectory.get("branching", False),
})
else:
summary_data["n_lineages"] = self.noise.sample_count(1) + 1
summary_data["pseudotime_range"] = [0.0, 1.0]
summary_data["branching_detected"] = self.noise.coin_flip(0.3)
return IntermediateOutput(
output_type=OutputType.TRAJECTORY_RESULT,
step_index=idx,
quality_score=quality,
summary="Trajectory / pseudotime analysis complete",
data=summary_data,
uncertainty=0.2 if has_trajectory else 0.6,
artifacts_available=["pseudotime_values", "lineage_graph"],
)
def _pathway_enrichment(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
true_pathways = s.biology.true_pathways
noise_level = 0.15
observed: Dict[str, float] = {}
for pw, activity in true_pathways.items():
observed[pw] = activity + float(self.noise.rng.normal(0, noise_level))
for i in range(self.noise.sample_count(2)):
observed[f"FP_PATHWAY_{i}"] = float(self.noise.rng.uniform(0.3, 0.6))
top = sorted(observed.items(), key=lambda kv: kv[1], reverse=True)[:15]
return IntermediateOutput(
output_type=OutputType.PATHWAY_RESULT,
step_index=idx,
quality_score=self.noise.quality_degradation(0.8, [0.95]),
summary=f"Pathway enrichment: {len(top)} significant pathways",
data={
"method": action.method or "GSEA",
"top_pathways": [
{"pathway": p, "score": round(s, 3)} for p, s in top
],
},
uncertainty=noise_level,
artifacts_available=["enrichment_table"],
)
def _regulatory_network(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
true_net = s.biology.true_regulatory_network
n_edges_true = sum(len(v) for v in true_net.values())
noise_edges = self.noise.sample_count(max(5, int(n_edges_true * 0.3)))
return IntermediateOutput(
output_type=OutputType.NETWORK_RESULT,
step_index=idx,
quality_score=self.noise.quality_degradation(0.6, [0.9]),
summary=f"Regulatory network inferred: {n_edges_true + noise_edges} edges",
data={
"method": action.method or "SCENIC",
"n_regulons": len(true_net) + self.noise.sample_count(3),
"n_edges": n_edges_true + noise_edges,
"top_regulators": list(true_net.keys())[:10],
},
uncertainty=0.35,
artifacts_available=["regulon_table", "grn_adjacency"],
)
def _marker_selection(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
true_markers = list(s.biology.true_markers)
noise_level = 0.2
observed_markers = [
m for m in true_markers if not self.noise.coin_flip(noise_level)
]
fp = self.noise.generate_false_positives(200, 0.01)
observed_markers.extend(fp)
return IntermediateOutput(
output_type=OutputType.MARKER_RESULT,
step_index=idx,
quality_score=self.noise.quality_degradation(0.75, [0.9]),
summary=f"Selected {len(observed_markers)} candidate markers",
data={
"markers": observed_markers[:20],
"n_candidates": len(observed_markers),
},
uncertainty=noise_level,
artifacts_available=["marker_list"],
)
def _validate_marker(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
marker = action.parameters.get("marker", "unknown")
is_true = marker in s.biology.true_markers
validation_correct = not self.noise.coin_flip(0.1)
validated = is_true == validation_correct
return IntermediateOutput(
output_type=OutputType.VALIDATION_RESULT,
step_index=idx,
quality_score=0.9 if validation_correct else 0.4,
summary=f"Marker {marker}: {'validated' if validated else 'not validated'}",
data={
"marker": marker,
"validated": validated,
"assay": action.method or "qPCR",
"effect_size": self.noise.sample_qc_metric(
1.5 if is_true else 0.2, 0.3, -0.5, 5.0
),
},
artifacts_available=["validation_data"],
)
def _design_followup(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
return IntermediateOutput(
output_type=OutputType.FOLLOWUP_DESIGN,
step_index=idx,
summary="Follow-up experiment design proposed",
data={"proposal": action.parameters},
artifacts_available=["followup_proposal"],
)
def _subagent_review(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
return IntermediateOutput(
output_type=OutputType.SUBAGENT_REPORT,
step_index=idx,
summary=f"Subagent review ({action.invoked_subagent or 'general'})",
data={"subagent": action.invoked_subagent, "notes": "Review complete."},
artifacts_available=["subagent_report"],
)
def _synthesize_conclusion(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
return IntermediateOutput(
output_type=OutputType.CONCLUSION,
step_index=idx,
summary="Conclusion synthesised from pipeline evidence",
data={"claims": action.parameters.get("claims", [])},
artifacts_available=["conclusion_report"],
)
def _default(
self, action: ExperimentAction, s: FullLatentState, idx: int
) -> IntermediateOutput:
return IntermediateOutput(
output_type=OutputType.FAILURE_REPORT,
step_index=idx,
success=False,
summary=f"Unhandled action type: {action.action_type}",
data={},
)
# ── helpers ─────────────────────────────────────────────────────────
def _random_partition(self, total: int, k: int) -> List[int]:
if k <= 0:
return []
fracs = self.noise.rng.dirichlet(alpha=[1.0] * k)
sizes = [max(1, int(total * f)) for f in fracs]
diff = total - sum(sizes)
sizes[0] += diff
return sizes
_HANDLERS = {
ActionType.COLLECT_SAMPLE: OutputGenerator._collect_sample,
ActionType.SELECT_COHORT: OutputGenerator._select_cohort,
ActionType.PREPARE_LIBRARY: OutputGenerator._prepare_library,
ActionType.CULTURE_CELLS: OutputGenerator._culture_cells,
ActionType.PERTURB_GENE: OutputGenerator._perturb,
ActionType.PERTURB_COMPOUND: OutputGenerator._perturb,
ActionType.SEQUENCE_CELLS: OutputGenerator._sequence_cells,
ActionType.RUN_QC: OutputGenerator._run_qc,
ActionType.FILTER_DATA: OutputGenerator._filter_data,
ActionType.NORMALIZE_DATA: OutputGenerator._normalize_data,
ActionType.INTEGRATE_BATCHES: OutputGenerator._integrate_batches,
ActionType.CLUSTER_CELLS: OutputGenerator._cluster_cells,
ActionType.DIFFERENTIAL_EXPRESSION: OutputGenerator._differential_expression,
ActionType.TRAJECTORY_ANALYSIS: OutputGenerator._trajectory_analysis,
ActionType.PATHWAY_ENRICHMENT: OutputGenerator._pathway_enrichment,
ActionType.REGULATORY_NETWORK_INFERENCE: OutputGenerator._regulatory_network,
ActionType.MARKER_SELECTION: OutputGenerator._marker_selection,
ActionType.VALIDATE_MARKER: OutputGenerator._validate_marker,
ActionType.DESIGN_FOLLOWUP: OutputGenerator._design_followup,
ActionType.REQUEST_SUBAGENT_REVIEW: OutputGenerator._subagent_review,
ActionType.SYNTHESIZE_CONCLUSION: OutputGenerator._synthesize_conclusion,
}
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