bulktrajblend-trajectory-interpolation

Key Notes

• 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.
• 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.
• 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.