Bulk RNA-seq deconvolution with Bulk2Single
Overview
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.
Instructions
- Load libraries and data
- 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']).
- Initialise the Bulk2Single model
- 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). - Explain GPU selection (gpu=-1 forces CPU) and how bulk_group names align with column IDs in the bulk matrix.
- Estimate cell fractions
- Call model.predicted_fraction() to run the integrated TAPE estimator, then plot stacked bar charts per sample to validate proportions. - Encourage saving the fraction table for downstream reporting (df.to_csv(...)).
- Preprocess for beta-VAE
- Execute model.bulk_preprocess_lazy(), model.single_preprocess_lazy(), and model.prepare_input() to produce matched feature spaces. - Clarify that the lazy preprocessing expects raw counts; skip if the user has already log-normalised data and instead provide aligned matrices manually.
- Train or load the beta-VAE
- Train with model.train(batch_size=512, learning_rate=1e-4, hidden_size=256, epoch_num=3500, vae_save_dir='...', vae_save_name='dg_vae', generate_save_dir='...', generate_save_name='dg'). - Mention early stopping via patience and how to resume by reloading weights with model.load('.../dg_vae.pth'). - Use model.plot_loss() to monitor convergence.
- Generate and filter synthetic cells
- Produce an AnnData using model.generate() and reduce noise through model.filtered(generate_adata, leiden_size=25). - Store the filtered AnnData (.write_h5ad) for reuse, noting it contains PCA embeddings in obsm['X_pca'].
- Benchmark against the reference atlas
- Plot cell-type compositions with ov.bulk2single.bulk2single_plot_cellprop(...) for both generated and reference data. - Assess correlation using ov.bulk2single.bulk2single_plot_correlation(single_data, generate_adata, celltype_key='clusters'). - Embed with generate_adata.obsm['X_mde'] = ov.pl.mde(generate_adata.obsm['X_pca']) and visualise via ov.pl.embedding(..., color=['clusters'], palette=ov.pl.sc_color()).
- Defensive validation
# Before Bulk2Single: verify gene name overlap between bulk and reference shared_genes = set(bulk_df.index) & set(adata.var_names) assert len(shared_genes) > 100, f"Only {len(shared_genes)} shared genes — check gene ID format (Ensembl vs symbol)" # Verify bulk_group column names match for g in bulk_group: assert g in bulk_df.columns, f"Bulk group '{g}' not found in bulk data columns" # Verify cell type key exists assert celltype_key in adata.obs.columns, f"Cell type column '{celltype_key}' not found in reference AnnData" - Troubleshooting tips
- If marker selection fails, increase top_marker_num or provide a curated marker list. - Alignment errors typically stem from mismatched bulk_group names—double-check column IDs in the bulk matrix. - Training on CPU can take several hours; advise switching gpu to an available CUDA device for speed.
Examples
- "Estimate cell fractions for PDAC bulk replicates and generate synthetic scRNA-seq using Bulk2Single."
- "Load a pre-trained Bulk2Single model, regenerate cells, and compare cluster proportions to the dentate gyrus atlas."
- "Plot correlation heatmaps between generated cells and reference clusters after filtering noisy synthetic cells."
References
- Tutorial notebook:
t_bulk2single.ipynb - Example data and weights:
omicverse_guide/docs/Tutorials-bulk2single/data/ - Quick copy/paste commands:
reference.md