# Bulk Rna Seq Deconvolution With Bulk2single

> 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.

- Skill: `omicverse/bulk-rna-seq-deconvolution-with-bulk2single` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add omicverse/bulk-rna-seq-deconvolution-with-bulk2single`
- Raw SKILL.md: https://api.skillmd.com/api/skills/omicverse/bulk-rna-seq-deconvolution-with-bulk2single/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: omicverse (https://skillmd.com/u/omicverse)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/omicverse/bulk-rna-seq-deconvolution-with-bulk2single

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# 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`](../../omicverse_guide/docs/Tutorials-bulk2single/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
1. **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']`).
2. **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.
3. **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(...)`).
4. **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.
5. **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.
6. **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']`.
7. **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())`.
8. **Defensive validation**
   ```python
   # 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"
   ```
9. **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`](../../omicverse_guide/docs/Tutorials-bulk2single/t_bulk2single.ipynb)
- Example data and weights: [`omicverse_guide/docs/Tutorials-bulk2single/data/`](../../omicverse_guide/docs/Tutorials-bulk2single/data/)
- Quick copy/paste commands: [`reference.md`](reference.md)