Optimal transport for mapping senescent cells in spatial transcriptomics
Spatial transcriptomics (ST) provides a unique opportunity to study cellular organization and cell-cell interactions at the molecular level. However, due to the low resolution of the sequencing data additional information is required to utilize this technology, especially for cases where only a few cells are present for important cell types. To enable the use of ST to study senescence we developed scDOT, which combines ST and single cell RNA-Sequencing (scRNA-Seq) to improve the ability to reconstruct single cell resolved spatial maps. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to existing and new lung ST data improves on prior methods and allows the identification of the spatial organization of senescent cells, the identification of their neighboring cells and the identification of novel genes involved in cell-cell interactions that may be driving senescence.
This script need no installation, but has the following requirements:
- PyTorch 1.9.0
- POT 0.9.3
nputs, including spatial transcriptomics data and single-cell transcriptomics data, must be formatted as AnnData objects. The basic usage of the script is as follows:
model = scDOT(sc_adata, st_adata)
optimizer = optim.Adam(model.parameters(), lr=1.0e-1)
iters = 10
for i in range(iters):
# Forward pass
NNLS_output, OT_output = model(sc_adata, st_adata)
ct = torch.tensor(sc_adata.obsm['cell_type'].values.T)
P_true = NNLS_output @ ct.float() # spots by cells
P = P_true/P_true.sum(0) # col (cell) sum to 1
loss_fn = torch.nn.CosineEmbeddingLoss()
loss = loss_fn(OT_output, P_true, torch.ones(P.shape[0]))
# Backward pass
optimizer.zero_grad()
loss.backward()
# Update the parameters
optimizer.step()MIT License
Copyright (c) 2020
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