BulkTrajBlend trajectory interpolation

BulkTrajBlend trajectory interpolation

Overview

Invoke this skill when users need to bridge gaps in single-cell developmental trajectories using matched bulk RNA-seq. It follows t_bulktrajblend.ipynb, showcasing how BulkTrajBlend deconvolves PDAC bulk samples, identifies overlapping communities with a GNN, and interpolates "interrupted" cell states.

Instructions

1. Prepare libraries and inputs
   • Import omicverse as ov, scanpy as sc, scvelo as scv, and helper functions like from omicverse.utils import mde; run ov.plot_set().
   • Load the reference scRNA-seq AnnData (scv.datasets.dentategyrus()) and raw bulk counts with ov.utils.read(...) followed by ov.bulk.Matrix_ID_mapping(...) for gene ID harmonisation.
2. Configure BulkTrajBlend
   • Instantiate ov.bulk2single.BulkTrajBlend(bulk_seq=bulk_df, single_seq=adata, bulk_group=['dg_d_1','dg_d_2','dg_d_3'], celltype_key='clusters').
   • Explain that bulk_group names correspond to raw bulk columns and the method expects unscaled counts.
3. Set beta-VAE expectations
   • Call bulktb.vae_configure(cell_target_num=100) (or pass a dictionary) to define expected cell counts per cluster. Mention that omitting the argument triggers TAPE-based estimation.
4. Train or load the beta-VAE
   • Use bulktb.vae_train(batch_size=512, learning_rate=1e-4, hidden_size=256, epoch_num=3500, vae_save_dir='...', vae_save_name='dg_btb_vae', generate_save_dir='...', generate_save_name='dg_btb').
   • Highlight resuming with bulktb.vae_load('.../​dg_btb_vae.pth') and the need to regenerate cells with consistent random seeds for reproducibility.
5. Generate synthetic cells
   • Produce filtered AnnData via bulktb.vae_generate(leiden_size=25) and inspect compositions with ov.bulk2single.bulk2single_plot_cellprop(...).
   • Save outputs to disk for reuse (adata.write_h5ad).
6. Configure and train the GNN
   • Call bulktb.gnn_configure(max_epochs=2000, use_rep='X', neighbor_rep='X_pca', gpu=0, ...) to set hyperparameters.
   • Train using bulktb.gnn_train(); reload checkpoints with bulktb.gnn_load('save_model/​gnn.pth').
   • Generate overlapping community assignments through bulktb.gnn_generate().
7. Visualise community structure
   • Create MDE embeddings: bulktb.nocd_obj.adata.obsm['X_mde'] = mde(bulktb.nocd_obj.adata.obsm['X_pca']).
   • Plot clusters vs. discovered communities using sc.pl.embedding(..., color=['clusters','nocd_n'], palette=ov.utils.pyomic_palette()) and filtered subsets excluding synthetic labels with hyphens.
8. Interpolate missing states
   • Run bulktb.interpolation('OPC') (replace with target lineage) to synthesise continuity, then preprocess the interpolated AnnData (HVG selection, scaling, PCA).
   • Compute embeddings with mde, visualise with ov.utils.embedding, and compare to the original atlas.
9. Analyse trajectories
   • Initialise ov.single.pyVIA on both original and interpolated data to derive pseudotime, followed by get_pseudotime, sc.pp.neighbors, ov.utils.cal_paga, and ov.utils.plot_paga for topology validation.
10. Troubleshooting tips
    • If the VAE collapses (high reconstruction loss), lower learning_rate or reduce hidden_size.
    • Ensure the same generated dataset is used before calling gnn_train; regenerating cells changes the graph and can break checkpoint loading.
    • Sparse clusters may need adjusted cell_target_num thresholds or a smaller leiden_size filter to retain rare populations.

Examples

• "Train BulkTrajBlend on PDAC cohorts, then interpolate missing OPC states in the trajectory."
• "Load saved beta-VAE and GNN weights to regenerate overlapping communities and plot cluster vs. nocd labels."
• "Run VIA on interpolated cells and compare PAGA graphs with the original scRNA-seq trajectory."

References

• Tutorial notebook: t_bulktrajblend.ipynb
• Example datasets and checkpoints: omicverse_guide/​docs/​Tutorials-bulk2single/​data/
• Quick copy/​paste commands: reference.md