bulktrajblend-trajectory-interpolation

name	bulktrajblend-trajectory-interpolation
title	BulkTrajBlend trajectory interpolation
description	Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.

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.