Segger Model Inference

Overview

Inference with the trained Segger model involves using the learned Graph Neural Network to predict transcript-to-cell associations in spatial transcriptomics data. The inference process transforms spatial data into cell segmentation results through link prediction, enabling both cell identification and fragment detection.

Inference Pipeline

Overall Workflow

Trained Model → Spatial Data → Graph Construction → Node Embeddings → Similarity Scores → Cell Assignment → Post-processing
     ↓              ↓              ↓                ↓              ↓              ↓              ↓
Learned Weights   Transcripts    Heterogeneous   GNN Forward    Link Prediction  Thresholding   Final Results
                 Boundaries     Graphs          Pass            Scores           Filtering      + Fragments

Key Steps

1. Model Loading: Load trained Segger model from checkpoint
2. Data Preparation: Construct heterogeneous graphs from spatial data
3. Embedding Generation: Generate node embeddings using the trained model
4. Similarity Computation: Calculate transcript-to-cell similarity scores
5. Assignment Decision: Assign transcripts to cells based on confidence scores
6. Fragment Detection: Group unassigned transcripts into fragments

Model Loading

Loading Trained Model

from segger.models.segger_model import Segger
import torch

# Load trained model
model = Segger(
    num_tx_tokens=5000,
    init_emb=16,
    hidden_channels=64,
    num_mid_layers=3,
    out_channels=32,
    heads=4
)

# Load trained weights
checkpoint = torch.load('path/to/checkpoint.ckpt')
model.load_state_dict(checkpoint['state_dict'])
model.eval()  # Set to evaluation mode

Model Configuration

Ensure inference parameters match training configuration:

# Verify model configuration matches training
assert model.num_tx_tokens == 5000, "Vocabulary size mismatch"
assert model.hidden_channels == 64, "Hidden dimension mismatch"
assert model.out_channels == 32, "Output dimension mismatch"
assert model.heads == 4, "Attention heads mismatch"

Data Preparation

Graph Construction

Inference requires the same graph structure used during training:

# Construct heterogeneous graph for inference
data = {
    "tx": {  # Transcript nodes
        "id": transcript_ids,
        "pos": spatial_coordinates,
        "x": feature_vectors
    },
    "bd": {  # Boundary nodes
        "id": boundary_ids,
        "pos": centroid_coordinates,
        "x": geometric_features
    },
    "tx,neighbors,tx": {  # Transcript proximity edges
        "edge_index": neighbor_connections
    },
    "tx,belongs,bd": {  # Transcript-boundary edges (for inference)
        "edge_index": containment_relationships
    }
}

Feature Processing

Transcript Features

# Use same feature processing as training
if has_scrnaseq_embeddings:
    transcript_features = gene_celltype_embeddings[gene_labels]
else:
    transcript_features = embedding_layer(gene_token_ids)

Boundary Features

# Compute geometric features for boundaries
def compute_boundary_features(boundary_polygons):
    features = []
    for polygon in boundary_polygons:
        area_val = polygon.area
        convex_hull = polygon.convex_hull
        convexity = convex_hull.area / area_val

        # Minimum bounding rectangle
        mbr = polygon.minimum_rotated_rectangle
        mbr_area = mbr.area

        # Envelope (axis-aligned bounding box)
        envelope = polygon.envelope
        env_area = envelope.area
        elongation = mbr_area / env_area

        # Circularity
        min_radius = compute_minimum_bounding_radius(polygon)
        circularity = area_val / (min_radius ** 2)

        features.append([area_val, convexity, elongation, circularity])

    return torch.tensor(features, dtype=torch.float32)

Inference Process

Forward Pass

# Generate node embeddings
with torch.no_grad():
    embeddings = model(data.x, data.edge_index)

# Separate transcript and boundary embeddings
tx_embeddings = embeddings[data.tx_mask]
bd_embeddings = embeddings[data.bd_mask]

Similarity Score Computation

Transcript-to-Cell Similarity

def compute_similarity_scores(tx_embeddings, bd_embeddings, edge_index):
    """Compute similarity scores between transcripts and boundaries."""

    # Extract source and target indices
    tx_indices = edge_index[0]
    bd_indices = edge_index[1]

    # Get embeddings for connected nodes
    tx_emb = tx_embeddings[tx_indices]
    bd_emb = bd_embeddings[bd_indices]

    # Compute dot product similarity
    similarity_scores = torch.sum(tx_emb * bd_emb, dim=1)

    # Apply sigmoid for probability
    probabilities = torch.sigmoid(similarity_scores)

    return probabilities, similarity_scores

Receptive Field Construction

def construct_receptive_field(transcripts, boundaries, k_bd=3, dist_bd=10.0):
    """Construct receptive field for transcript-to-cell assignment."""

    # Find nearest boundary cells for each transcript
    from sklearn.neighbors import NearestNeighbors

    # Extract coordinates
    tx_coords = transcripts[['x', 'y']].values
    bd_coords = boundaries[['centroid_x', 'centroid_y']].values

    # Build nearest neighbor index
    nn = NearestNeighbors(n_neighbors=k_bd, radius=dist_bd)
    nn.fit(bd_coords)

    # Find neighbors
    distances, indices = nn.kneighbors(tx_coords)

    # Filter by distance threshold
    mask = distances <= dist_bd
    filtered_indices = []
    filtered_distances = []

    for i, (dist, idx) in enumerate(zip(distances, indices)):
        valid_mask = mask[i]
        filtered_indices.append(idx[valid_mask])
        filtered_distances.append(dist[valid_mask])

    return filtered_indices, filtered_distances

Cell Assignment

Assignment Decision

def assign_transcripts_to_cells(similarity_scores, score_threshold=0.7):
    """Assign transcripts to cells based on similarity scores."""

    # Find best matching cell for each transcript
    best_scores, best_cells = torch.max(similarity_scores, dim=1)

    # Apply confidence threshold
    confident_mask = best_scores >= score_threshold

    # Create assignment results
    assignments = {
        'transcript_id': [],
        'cell_id': [],
        'confidence_score': [],
        'assigned': []
    }

    for i, (score, cell_id) in enumerate(zip(best_scores, best_cells)):
        assignments['transcript_id'].append(i)
        assignments['cell_id'].append(cell_id.item())
        assignments['confidence_score'].append(score.item())
        assignments['assigned'].append(confident_mask[i].item())

    return assignments

Confidence Score Analysis

def analyze_confidence_scores(scores):
    """Analyze distribution of confidence scores."""

    import numpy as np
    from scipy import stats

    # Convert to numpy for analysis
    scores_np = scores.detach().cpu().numpy()

    # Basic statistics
    stats_summary = {
        'mean': np.mean(scores_np),
        'std': np.std(scores_np),
        'min': np.min(scores_np),
        'max': np.max(scores_np),
        'median': np.median(scores_np)
    }

    # Percentiles
    percentiles = [25, 50, 75, 90, 95, 99]
    for p in percentiles:
        stats_summary[f'p{p}'] = np.percentile(scores_np, p)

    # Find knee point for automatic thresholding
    # Use the method from the Segger paper
    knee_point = find_knee_point(scores_np)
    stats_summary['knee_point'] = knee_point

    return stats_summary

def find_knee_point(scores):
    """Find knee point in score distribution for automatic thresholding."""

    from kneed import KneeLocator

    # Sort scores
    sorted_scores = np.sort(scores)

    # Create cumulative distribution
    cumulative = np.arange(1, len(sorted_scores) + 1) / len(sorted_scores)

    # Find knee point
    kneedle = KneeLocator(
        sorted_scores, cumulative, 
        S=1.0, curve='concave', direction='increasing'
    )

    return kneedle.knee if kneedle.knee else np.median(scores)

Fragment Detection

Unassigned Transcript Handling

def detect_fragments(unassigned_transcripts, k_tx=4, dist_tx=5.0, similarity_threshold=0.5):
    """Group unassigned transcripts into fragments."""

    # Construct transcript-transcript similarity graph
    fragment_graph = construct_fragment_graph(
        unassigned_transcripts, k_tx, dist_tx, similarity_threshold
    )

    # Find connected components
    from scipy.sparse.csgraph import connected_components

    n_components, labels = connected_components(fragment_graph, directed=False)

    # Assign fragment IDs
    fragment_assignments = {}
    for i, label in enumerate(labels):
        transcript_id = unassigned_transcripts[i]
        fragment_assignments[transcript_id] = f"fragment_{label}"

    return fragment_assignments, n_components

Fragment Graph Construction

def construct_fragment_graph(transcripts, k_tx, dist_tx, similarity_threshold):
    """Construct similarity graph for unassigned transcripts."""

    # Extract coordinates and features
    coords = transcripts[['x', 'y']].values
    features = transcripts['features'].values

    # Build nearest neighbor graph
    nn = NearestNeighbors(n_neighbors=k_tx, radius=dist_tx)
    nn.fit(coords)

    # Find neighbors
    distances, indices = nn.radius_neighbors(coords, radius=dist_tx)

    # Compute similarity scores
    edges = []
    for i, neighbors in enumerate(indices):
        for j in neighbors:
            if i != j:
                # Compute feature similarity
                sim_score = compute_feature_similarity(features[i], features[j])

                # Add edge if similarity exceeds threshold
                if sim_score >= similarity_threshold:
                    edges.append((i, j, sim_score))

    # Convert to sparse matrix
    from scipy.sparse import csr_matrix

    if edges:
        rows, cols, data = zip(*edges)
        n_transcripts = len(transcripts)
        fragment_graph = csr_matrix((data, (rows, cols)), shape=(n_transcripts, n_transcripts))
    else:
        fragment_graph = csr_matrix((len(transcripts), len(transcripts)))

    return fragment_graph

def compute_feature_similarity(feature1, feature2):
    """Compute similarity between transcript features."""

    # Cosine similarity
    dot_product = np.dot(feature1, feature2)
    norm1 = np.linalg.norm(feature1)
    norm2 = np.linalg.norm(feature2)

    if norm1 == 0 or norm2 == 0:
        return 0.0

    similarity = dot_product / (norm1 * norm2)
    return similarity

Batch Processing

Large Dataset Handling

def batch_inference(model, data_loader, device='cuda'):
    """Perform inference on large datasets in batches."""

    model.to(device)
    model.eval()

    all_results = []

    with torch.no_grad():
        for batch in data_loader:
            # Move batch to device
            batch = batch.to(device)

            # Generate embeddings
            embeddings = model(batch.x, batch.edge_index)

            # Compute similarity scores
            scores = compute_batch_similarities(batch, embeddings)

            # Store results
            batch_results = {
                'transcript_ids': batch.transcript_ids,
                'cell_ids': batch.cell_ids,
                'similarity_scores': scores,
                'batch_idx': batch.batch
            }

            all_results.append(batch_results)

    # Combine results from all batches
    combined_results = combine_batch_results(all_results)

    return combined_results

Memory Management

def optimize_memory_usage(batch_size, model_size):
    """Optimize memory usage during inference."""

    # Estimate memory requirements
    estimated_memory = estimate_inference_memory(batch_size, model_size)

    # Adjust batch size if needed
    if estimated_memory > available_memory:
        optimal_batch_size = find_optimal_batch_size(model_size, available_memory)
        print(f"Reducing batch size from {batch_size} to {optimal_batch_size}")
        return optimal_batch_size

    return batch_size

def estimate_inference_memory(batch_size, model_size):
    """Estimate memory usage for inference."""

    # Model parameters
    model_memory = model_size * 4  # 4 bytes per float32

    # Activations (approximate)
    activation_memory = batch_size * 1000 * 64 * 4  # Assume 1000 nodes, 64 features

    # Total memory
    total_memory = model_memory + activation_memory

    return total_memory

Post-processing

Result Aggregation

def aggregate_inference_results(assignments, fragments):
    """Aggregate inference results into final segmentation."""

    # Combine cell assignments and fragment assignments
    final_results = {
        'transcript_id': [],
        'final_cell_id': [],
        'assignment_type': [],  # 'cell' or 'fragment'
        'confidence_score': []
    }

    # Add cell assignments
    for tx_id, cell_id, score, assigned in zip(
        assignments['transcript_id'],
        assignments['cell_id'],
        assignments['confidence_score'],
        assignments['assigned']
    ):
        if assigned:
            final_results['transcript_id'].append(tx_id)
            final_results['final_cell_id'].append(cell_id)
            final_results['assignment_type'].append('cell')
            final_results['confidence_score'].append(score)

    # Add fragment assignments
    for tx_id, fragment_id in fragments.items():
        final_results['transcript_id'].append(tx_id)
        final_results['final_cell_id'].append(fragment_id)
        final_results['assignment_type'].append('fragment')
        final_results['confidence_score'].append(0.0)  # No confidence for fragments

    return final_results

Quality Control

def quality_control_checks(results, min_transcripts_per_cell=5):
    """Perform quality control checks on inference results."""

    # Count transcripts per cell
    from collections import Counter
    cell_counts = Counter(results['final_cell_id'])

    # Filter cells with too few transcripts
    valid_cells = {cell_id: count for cell_id, count in cell_counts.items() 
                   if count >= min_transcripts_per_cell}

    # Filter results
    filtered_results = {
        'transcript_id': [],
        'final_cell_id': [],
        'assignment_type': [],
        'confidence_score': []
    }

    for i, cell_id in enumerate(results['final_cell_id']):
        if cell_id in valid_cells:
            for key in filtered_results:
                filtered_results[key].append(results[key][i])

    return filtered_results, valid_cells

Output Formats

Standard Output

def save_inference_results(results, output_path, format='parquet'):
    """Save inference results in specified format."""

    import pandas as pd

    # Convert to DataFrame
    df = pd.DataFrame(results)

    if format == 'parquet':
        df.to_parquet(output_path, index=False)
    elif format == 'csv':
        df.to_csv(output_path, index=False)
    elif format == 'h5ad':
        # Convert to AnnData format
        adata = convert_to_anndata(df)
        adata.write(output_path)
    else:
        raise ValueError(f"Unsupported format: {format}")

    print(f"Results saved to {output_path}")

def convert_to_anndata(results_df):
    """Convert results to AnnData format for downstream analysis."""

    import scanpy as sc
    import anndata as ad

    # Group by cell ID
    cell_groups = results_df.groupby('final_cell_id')

    # Create cell-gene matrix
    cell_gene_matrix = []
    cell_ids = []

    for cell_id, group in cell_groups:
        # Count transcripts per gene
        gene_counts = group['gene_name'].value_counts()
        cell_gene_matrix.append(gene_counts)
        cell_ids.append(cell_id)

    # Convert to DataFrame
    cell_gene_df = pd.DataFrame(cell_gene_matrix, index=cell_ids)
    cell_gene_df = cell_gene_df.fillna(0)

    # Create AnnData object
    adata = ad.AnnData(X=cell_gene_df.values, 
                       obs=pd.DataFrame(index=cell_ids),
                       var=pd.DataFrame(index=cell_gene_df.columns))

    return adata

Performance Optimization

GPU Acceleration

def optimize_gpu_inference(model, data, device='cuda'):
    """Optimize GPU inference performance."""

    # Move model and data to GPU
    model = model.to(device)
    data = data.to(device)

    # Enable CUDA optimizations
    torch.backends.cudnn.benchmark = True

    # Use mixed precision if available
    if hasattr(torch, 'autocast'):
        with torch.autocast(device_type='cuda', dtype=torch.float16):
            embeddings = model(data.x, data.edge_index)
    else:
        embeddings = model(data.x, data.edge_index)

    return embeddings

Parallel Processing

def parallel_inference(model, data_list, num_workers=4):
    """Perform inference in parallel across multiple workers."""

    from concurrent.futures import ProcessPoolExecutor
    import multiprocessing as mp

    # Set multiprocessing start method
    mp.set_start_method('spawn', force=True)

    # Split data across workers
    chunk_size = len(data_list) // num_workers
    data_chunks = [data_list[i:i+chunk_size] for i in range(0, len(data_list), chunk_size)]

    # Process in parallel
    with ProcessPoolExecutor(max_workers=num_workers) as executor:
        futures = [executor.submit(process_chunk, model, chunk) for chunk in data_chunks]
        results = [future.result() for future in futures]

    # Combine results
    combined_results = combine_chunk_results(results)

    return combined_results

Troubleshooting

Common Issues

Memory Errors

# Solutions:
# 1. Reduce batch size
batch_size = 1  # Reduce from default

# 2. Use gradient checkpointing
model.gradient_checkpointing_enable()

# 3. Clear GPU cache
torch.cuda.empty_cache()

Slow Inference

# Solutions:
# 1. Enable CUDA optimizations
torch.backends.cudnn.benchmark = True

# 2. Use mixed precision
with torch.autocast(device_type='cuda'):
    embeddings = model(data.x, data.edge_index)

# 3. Optimize data loading
data_loader = DataLoader(dataset, batch_size=batch_size, 
                        num_workers=4, pin_memory=True)

Poor Quality Results

# Solutions:
# 1. Check model configuration matches training
# 2. Verify data preprocessing is identical
# 3. Adjust confidence threshold
# 4. Check for data distribution shifts

Best Practices

Inference Configuration

1. Model Consistency: Ensure inference parameters match training exactly
2. Data Preprocessing: Use identical preprocessing as training
3. Confidence Thresholds: Start with recommended thresholds and adjust based on data
4. Memory Management: Monitor GPU memory usage and optimize batch sizes

Quality Assurance

1. Validation Checks: Verify inference results against known ground truth
2. Confidence Analysis: Analyze score distributions for appropriate thresholds
3. Fragment Detection: Enable fragment detection for comprehensive coverage
4. Post-processing: Apply quality control filters to remove low-quality assignments

Performance Optimization

1. GPU Utilization: Maximize GPU usage with appropriate batch sizes
2. Parallel Processing: Use multiple workers for data loading
3. Memory Efficiency: Optimize memory usage with mixed precision
4. Batch Processing: Process large datasets in manageable chunks

Future Enhancements

Planned inference improvements include:

• Real-time Inference: Streaming inference for live data
• Model Compression: Quantized models for faster inference
• Distributed Inference: Multi-node inference capabilities
• Adaptive Thresholding: Dynamic confidence thresholds based on data
• Uncertainty Quantification: Confidence intervals for predictions