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PCAScGPTExtractor — Projects FrozenScGPTExtractor output onto
the first n_dims principal components of scGPT gene embeddings.
Instead of slicing the first n_dims (arbitrary), PCA captures the
dominant variation direction in gene embedding space:
gene_proj = PCA(gene_emb, n_dims) # (G, 1)
features = delta_attn @ gene_proj # (B, G, 1)
"""
import torch
import torch.nn as nn
class PCAScGPTExtractor(nn.Module):
def __init__(self, base_extractor, n_dims: int = 1):
super().__init__()
self.base = base_extractor
self.n_dims = n_dims
self.scgpt_d_model = n_dims
self.n_hvg = base_extractor.n_hvg
self._pca_V = None # (512, n_dims), computed lazily
@torch.no_grad()
def _ensure_pca(self, device):
if self._pca_V is not None:
return
valid_ids = self.base.hvg_to_scgpt_id[self.base.hvg_to_scgpt_id >= 0]
gene_emb = self.base.scgpt_model.encoder(
valid_ids.unsqueeze(0).to(device)
).squeeze(0) # (G_valid, 512)
centered = gene_emb - gene_emb.mean(dim=0)
U, S, V = torch.pca_lowrank(centered, q=max(self.n_dims, 6))
self._pca_V = V[:, :self.n_dims].to(device) # (512, n_dims)
explained = (S[:self.n_dims] ** 2).sum() / (centered ** 2).sum()
print(f"[PCA] gene_emb: {self.base.scgpt_d_model}D -> {self.n_dims}D, "
f"explained variance: {explained:.4f}")
def extract(self, expression_values, gene_indices=None):
z = self.base.extract(expression_values, gene_indices) # (B, G, 512)
self._ensure_pca(z.device)
return torch.matmul(z, self._pca_V) # (B, G, n_dims)
def extract_attention_delta(self, control_expr, target_expr,
gene_indices=None, attn_layer=11,
use_rank_norm=True, multi_layer=""):
z = self.base.extract_attention_delta(
control_expr, target_expr, gene_indices,
attn_layer, use_rank_norm, multi_layer,
) # (B, G, 512)
self._ensure_pca(z.device)
return torch.matmul(z, self._pca_V) # (B, G, n_dims)
def get_missing_gene_mask(self, gene_indices=None):
return self.base.get_missing_gene_mask(gene_indices)
def train(self, mode=True):
super().train(mode)
self.base.train(mode)
return self
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