| name | bulk-rna-seq-deconvolution-with-bulk2single |
| title | Bulk RNA-seq deconvolution with Bulk2Single |
| description | Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq. |
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.utils.mde(generate_adata.obsm['X_pca']) and visualise via ov.utils.embedding(..., color=['clusters'], palette=ov.utils.pyomic_palette()).
- 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
