aman
acadbd780c
Full reproducible pipeline: .mcool + ChIP-seq bigwigs → latent embeddings → A/B compartment calls → cross-cell comparison. Key results (chr21, 25 kb, latent dim=32): - Test AUC=0.777, AP=0.759 (converged epoch 31/300) - GM12878 A/B silhouette (cosine) = 0.775 - IMR90 zero-shot silhouette = 0.443 - A-compartment bins stable across cell types (mean cosine Δ=0.042) - B-compartment bins shift substantially (mean cosine Δ=0.451) - 101 B→A and 70 A→B compartment switches GM12878→IMR90
33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
#!/usr/bin/env python3
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"""Shared VGAE encoder. Imported by train_vgae.py and encode_graph.py."""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch_geometric.nn import GCNConv
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class Encoder(nn.Module):
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"""Two-layer GCN encoder for VGAE with input BatchNorm.
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Architecture: BatchNorm → GCN(hidden) → ReLU → Dropout → GCN_mu / GCN_logstd
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The BatchNorm layer normalises raw ChIP-seq signals and its running statistics
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are saved in model.pt, so encode_graph.py applies identical normalisation to
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held-out cell lines without a separate scaler file.
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"""
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def __init__(self, in_dim: int, hidden: int, latent: int, dropout: float = 0.2):
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super().__init__()
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self.norm = nn.BatchNorm1d(in_dim)
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self.gc1 = GCNConv(in_dim, hidden, add_self_loops=True, normalize=True)
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self.gc_mu = GCNConv(hidden, latent, add_self_loops=True, normalize=True)
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self.gc_log = GCNConv(hidden, latent, add_self_loops=True, normalize=True)
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self.dropout = dropout
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def forward(self, x, edge_index):
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x = self.norm(x)
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h = F.relu(self.gc1(x, edge_index))
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h = F.dropout(h, p=self.dropout, training=self.training)
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return self.gc_mu(h, edge_index), self.gc_log(h, edge_index)
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