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single2spatial-spatial-mapping

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Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.

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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.

Instructions

  1. Import dependencies and style
    • Load omicverse as ov, scanpy as sc, anndata, pandas as pd, numpy as np, and matplotlib.pyplot as plt.
    • Call ov.utils.ov_plot_set() (or ov.plot_set() in older versions) to align plots with omicverse styling.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Assess spot-level outputs
    • Call st_model.spot_assess() to compute aggregated spot AnnData (sp_adata_spot) for QC.
    • Plot marker genes with sc.pl.embedding(sp_adata, basis='X_spatial', color=['REG1A', 'CLDN1', ...], frameon=False, ncols=4).
  7. 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.
    • Plot sp_adata coloured by Cell_type with palette=ov.utils.ov_palette()[11:] to show reconstructed assignments.
  8. Export results
    • Encourage saving generated AnnData objects (sp_adata.write_h5ad(...), sp_adata_spot.write_h5ad(...)) and derived CSV summaries for downstream reporting.
  9. 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.

Examples

  • "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."

References