OpenClaw-Medical-Skills 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.

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/bulk-to-single-deconvolution" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-bulk-rna-seq-deconvolution-with-bulk && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/bulk-to-single-deconvolution" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-bulk-rna-seq-deconvolution-with-bulk && rm -rf "$T"

manifest: skills/bulk-to-single-deconvolution/SKILL.md

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

Tutorial notebook:
```
t_bulk2single.ipynb
```

Example data and weights:

omicverse_guide/docs/Tutorials-bulk2single/data/

Quick copy/paste commands:
```
reference.md
```