bulk-rna-seq-deconvolution-with-bulk2single

Key Notes

• Use this skill when a user wants to reconstruct single-cell profiles from bulk RNA-seq together with a matched reference scRNA-seq atlas. It follows t_bulk2single.ipynb, which demonstrates how to harmonise PDAC bulk replicates, train the beta-VAE generator, and benchmark the output cells against dentate gyrus scRNA-seq.
• Import omicverse as ov, scanpy as sc, scvelo as scv, anndata, and matplotlib.pyplot as plt, then call ov.plot_set() to match omicverse styling.
• Read the bulk counts table with ov.read(...)/ov.utils.read(...) and harmonise gene identifiers via ov.bulk.Matrix_ID_mapping(<df>, 'genesets/pair_GRCm39.tsv').
• Load the reference scRNA-seq AnnData (e.g., scv.datasets.dentategyrus()) and confirm the cluster labels (stored in adata.obs['clusters']).
• Instantiate ov.bulk2single.Bulk2Single(bulk_data=bulk_df, single_data=adata, celltype_key='clusters', bulk_group=['dg_d_1', 'dg_d_2', 'dg_d_3'], top_marker_num=200, ratio_num=1, gpu=0).