Single2Spatial spatial mapping

Overview

Apply this skill when converting single-cell references into spatially resolved profiles. It follows

t_single2spatial.ipynb

, demonstrating how Single2Spatial trains on PDAC scRNA-seq and Visium data, reconstructs spot-level proportions, and visualises marker expression.

Import dependencies and style

Load

omicverse as ov

scanpy as sc

anndata

pandas as pd

numpy as np

, and

matplotlib.pyplot as plt

Load single-cell and spatial datasets
- Read processed matrices with
```
pd.read_csv(...)
```
  then create AnnData objects (
```
anndata.AnnData(raw_df.T)
```
  ).
- Attach metadata:
```
single_data.obs = pd.read_csv(...)[['Cell_type']]
```
  and
```
spatial_data.obs = pd.read_csv(... )
```
  containing coordinates and slide metadata.

Initialise Single2Spatial

Instantiate

ov.bulk2single.Single2Spatial(single_data=single_data, spatial_data=spatial_data, celltype_key='Cell_type', spot_key=['xcoord','ycoord'], gpu=0)

Note that inputs should be normalised/log-scaled scRNA-seq matrices; ensure
```
spot_key
```
matches spatial coordinate columns.

Train the deep-forest model
- Execute
```
st_model.train(spot_num=500, cell_num=10, df_save_dir='...', df_save_name='pdac_df', k=10, num_epochs=1000, batch_size=1000, predicted_size=32)
```
  to fit the mapper and generate reconstructed spatial AnnData (
```
sp_adata
```
  ).
- Explain that
```
spot_num
```
  defines sampled pseudo-spots per iteration and
```
cell_num
```
  controls per-spot cell draws.

Load pretrained weights

Use

st_model.load(modelsize=14478, df_load_dir='.../pdac_df.pth', k=10, predicted_size=32)

when checkpoints already exist to skip training.

Assess spot-level outputs

Plot marker genes with

sc.pl.embedding(sp_adata, basis='X_spatial', color=['REG1A', 'CLDN1', ...], frameon=False, ncols=4)

Visualise proportions and cell-type maps

Use

sc.pl.embedding(sp_adata_spot, basis='X_spatial', color=['Acinar cells', ...], frameon=False)

to highlight per-spot cell fractions.

Export results
- Encourage saving generated AnnData objects (
```
sp_adata.write_h5ad(...)
```
  ,
```
sp_adata_spot.write_h5ad(...)
```
  ) and derived CSV summaries for downstream reporting.
Troubleshooting tips
- If training diverges, reduce
```
learning_rate
```
  via keyword arguments or decrease
```
predicted_size
```
  to stabilise the forest.
- Ensure scRNA-seq inputs are log-normalised; raw counts can lead to scale mismatches and poor spatial predictions.
- Verify GPU availability when
```
gpu
```
  is non-zero; fallback to CPU by omitting the argument or setting
```
gpu=-1
```
  .

"Train Single2Spatial on PDAC scRNA-seq and Visium slides, then visualise REG1A and CLDN1 spatial expression."
"Load a saved Single2Spatial checkpoint to regenerate spot-level cell-type proportions for reporting."
"Plot reconstructed cell-type maps with omicverse palettes to compare against histology."

Example datasets and models:

omicverse_guide/docs/Tutorials-bulk2single/data/pdac/