Metrics

GGE provides a comprehensive suite of metrics for evaluating generated gene expression data. All metrics are computed per-gene and then aggregated.

Computation Spaces

GGE treats computation space as a first-class parameter (Paper Section 3.3):

Space	Description	When to Use
Raw Gene Space	Full ~5,000–20,000 dimensions	Gene-level interpretability needed
PCA Space	Reduced k-dimensional space	Primary distributional metrics (default: 50 PCs)
DEG Space	Restricted to differentially expressed genes	Biologically-targeted evaluation

Recommendation: Use multi-space evaluation strategy—PCA-50 for distributional metrics, DEG for biological focus.

Correlation Metrics

Pearson Correlation

Measures linear correlation between real and generated expression profiles.

from gge.metrics import PearsonCorrelation

metric = PearsonCorrelation()
result = metric.compute(real_data, generated_data)
print(f"Mean Pearson: {result.aggregate_value:.3f}")

Interpretation: Values range from -1 to 1. Higher values indicate better agreement.

Spearman Correlation

Rank-based correlation, robust to outliers and non-linear relationships.

from gge.metrics import SpearmanCorrelation

metric = SpearmanCorrelation()
result = metric.compute(real_data, generated_data)

Interpretation: Values range from -1 to 1. Higher values indicate better agreement.

R² (Coefficient of Determination)

Measures the proportion of variance in the real data explained by the generated data.

from gge.metrics import RSquared

metric = RSquared()
result = metric.compute(real_data, generated_data)

Interpretation: Values typically 0 to 1 (can be negative for poor fits). Higher is better.

Perturbation-Effect Correlation

(Paper Equation 1): Measures whether models capture the direction and magnitude of perturbation effects.

from gge import compute_perturbation_effect_correlation

# Compute control mean
control_mean = real_adata[real_adata.obs['condition'] == 'control'].X.mean(axis=0)

# Get perturbed samples
mask = real_adata.obs['condition'] != 'control'
rho = compute_perturbation_effect_correlation(
    real_perturbed=real_adata[mask].X,
    generated_perturbed=gen_adata[mask].X,
    control_mean=control_mean,
    method="pearson",  # or "spearman"
)
print(f"Perturbation-effect correlation: {rho:.3f}")

The formula is:

ρ_effect = corr(μ_real - μ_ctrl, μ_gen - μ_ctrl)

Why this matters: Computing correlation on raw expression means can be artificially high if control and perturbed conditions have similar expression. This metric focuses on whether the perturbation effect is captured.

Interpretation: Values range from -1 to 1. Higher values indicate the model better captures perturbation effects.

Distribution Metrics

Wasserstein-1 Distance (Earth Mover's Distance)

Measures the minimum "work" required to transform one distribution into another using L1 ground distance.

from gge.metrics import Wasserstein1Distance

metric = Wasserstein1Distance()
result = metric.compute(real_data, generated_data)

Interpretation: Non-negative values. Lower is better.

Wasserstein-2 Distance

Sinkhorn-regularized optimal transport distance with quadratic cost.

from gge.metrics import Wasserstein2Distance

metric = Wasserstein2Distance()
result = metric.compute(real_data, generated_data)

Interpretation: Non-negative values. Lower is better.

Maximum Mean Discrepancy (MMD)

Kernel-based two-sample test statistic using RBF kernel with median heuristic bandwidth.

from gge.metrics import MMDDistance

metric = MMDDistance()
result = metric.compute(real_data, generated_data)

Interpretation: Non-negative values. Lower indicates distributions are more similar.

Energy Distance

Statistical distance based on potential energy concepts.

from gge.metrics import EnergyDistance

metric = EnergyDistance()
result = metric.compute(real_data, generated_data)

Interpretation: Non-negative values. Lower is better.

Mean Squared Error (MSE)

Simple reconstruction metric measuring average squared difference.

from gge.metrics import MSEDistance

metric = MSEDistance()
result = metric.compute(real_data, generated_data)

Interpretation: Non-negative values. Lower is better.

Metric Summary Table

Metric	Type	Range	Optimal
Pearson	Correlation	[-1, 1]	Higher
Spearman	Correlation	[-1, 1]	Higher
R²	Reconstruction	(-∞, 1]	Higher
Perturbation-Effect	Correlation	[-1, 1]	Higher
MSE	Reconstruction	[0, ∞)	Lower
Wasserstein-1	Distribution	[0, ∞)	Lower
Wasserstein-2	Distribution	[0, ∞)	Lower
MMD	Distribution	[0, ∞)	Lower
Energy	Distribution	[0, ∞)	Lower

Using Multiple Metrics

from gge import evaluate

results = evaluate(
    real_path="real.h5ad",
    generated_path="generated.h5ad",
    condition_columns=["perturbation"],
    metrics=["pearson", "spearman", "wasserstein_1", "mmd"]
)

Custom Metrics

You can implement custom metrics by extending BaseMetric:

from gge.metrics.base_metric import BaseMetric, MetricResult

class MyCustomMetric(BaseMetric):
    name = "custom"
    higher_is_better = True

    def _compute_per_gene(self, real, generated):
        # Your computation here
        return per_gene_values