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STELLAR-like Spatial GNN for hierarchical cell annotation.
Architecture:
1. Build KNN spatial graph (precomputed)
2. Gene expression encoder (Linear → hidden)
3. GCN message passing layers (aggregate neighbor features)
4. Hierarchical classification heads (same residual design as mjm_1)
5. Reconstruction decoder
The spatial graph encodes cell neighborhood structure — cells that are
physically close share information through message passing, compensating
for the limited gene panel in MERFISH data.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy.spatial import cKDTree
# ── Graph construction ──────────────────────────────────────────────────────
def build_knn_graph(spatial_coords, k=15):
"""
Build a KNN spatial graph from 2D coordinates.
Returns edge_index [2, E] as a LongTensor (COO format).
"""
tree = cKDTree(spatial_coords)
_, indices = tree.query(spatial_coords, k=k + 1) # +1 because self is included
n = len(spatial_coords)
src = np.repeat(np.arange(n), k)
dst = indices[:, 1:].flatten() # exclude self-loop
edge_index = np.stack([src, dst], axis=0)
return torch.from_numpy(edge_index).long()
# ── GCN Layer (pure PyTorch) ────────────────────────────────────────────────
class GCNConv(nn.Module):
"""Simple GCN convolution: h' = D^{-1} A X W + b (mean aggregation)."""
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
def forward(self, x, edge_index):
"""
x: [N, in_dim]
edge_index: [2, E] (src → dst)
"""
src, dst = edge_index
N = x.size(0)
# Transform
h = self.linear(x) # [N, out_dim]
# Aggregate (mean of neighbors)
# Use scatter_mean via index_add
agg = torch.zeros_like(h)
agg.index_add_(0, dst, h[src])
# Degree normalization
deg = torch.zeros(N, device=x.device)
deg.index_add_(0, dst, torch.ones(dst.size(0), device=x.device))
deg = deg.clamp(min=1).unsqueeze(-1)
agg = agg / deg
return agg
# ── Spatial GNN Model ───────────────────────────────────────────────────────
class SpatialGNN(nn.Module):
"""
STELLAR-inspired spatial GNN for hierarchical cell annotation.
Pipeline:
X [B, 140] → gene_encoder → h0 [B, hidden]
h0 → GCN_1 → h1 → GCN_2 → h2 (spatial context aggregation)
h2 → head_class → logits_class [B, 3]
h2 → head_subclass → logits_subclass [B, 24] (+ residual from class)
h2 → head_supertype→ logits_supertype [B, 137] (+ residual from subclass)
h2 → recon_decoder → x_hat [B, 140]
"""
def __init__(self,
input_dim=140,
hidden_dim=256,
latent_dim=128,
n_gcn_layers=2,
dropout=0.3,
output_num=[3, 24, 137]):
super().__init__()
# Gene expression encoder
self.gene_encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.SiLU(),
nn.Dropout(dropout),
)
# GCN layers
self.gcn_layers = nn.ModuleList()
self.gcn_norms = nn.ModuleList()
for _ in range(n_gcn_layers):
self.gcn_layers.append(GCNConv(hidden_dim, hidden_dim))
self.gcn_norms.append(nn.LayerNorm(hidden_dim))
self.gcn_dropout = nn.Dropout(dropout)
# Projection to latent
self.to_latent = nn.Linear(hidden_dim, latent_dim)
# Hierarchical classification heads (with cross-level feature residual)
dec_dim = latent_dim
self.dec1 = nn.Sequential(nn.Linear(dec_dim, dec_dim), nn.SiLU(), nn.Dropout(dropout))
self.dec2 = nn.Sequential(nn.Linear(dec_dim, dec_dim), nn.SiLU(), nn.Dropout(dropout))
self.dec3 = nn.Sequential(nn.Linear(dec_dim, dec_dim), nn.SiLU(), nn.Dropout(dropout))
self.head1 = nn.Linear(dec_dim, output_num[0])
self.head2 = nn.Linear(dec_dim, output_num[1])
self.head3 = nn.Linear(dec_dim, output_num[2])
# Reconstruction
self.recon_decoder = nn.Sequential(
nn.Linear(latent_dim, hidden_dim),
nn.SiLU(),
nn.Linear(hidden_dim, input_dim),
)
def forward(self, x, edge_index):
"""
Args:
x: [N, input_dim] gene expression (log1p normalized)
edge_index: [2, E] spatial KNN graph
Returns:
recon: [N, input_dim]
logits: [logits_class, logits_subclass, logits_supertype]
z: [N, latent_dim]
"""
# Encode gene expression
h = self.gene_encoder(x)
# GCN message passing with residual connections
for gcn, norm in zip(self.gcn_layers, self.gcn_norms):
h_new = gcn(h, edge_index)
h = norm(h + h_new) # residual + norm
h = F.silu(h)
h = self.gcn_dropout(h)
# Project to latent
z = self.to_latent(h)
# Hierarchical decoding with feature residuals
c1 = self.dec1(z)
logits1 = self.head1(c1)
c2 = self.dec2(z) + c1
logits2 = self.head2(c2)
c3 = self.dec3(z) + c2
logits3 = self.head3(c3)
# Reconstruction
recon = self.recon_decoder(z)
return recon, [logits1, logits2, logits3], z
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