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bulk-rna-seq-deconvolution-with-bulk2single（使用 bulk2single 进行批量 rna seq 反卷积）

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bulk-rna-seq-deconvolution-with-bulk2single 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息，适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态，以及是否会触发联网、命令执行或文件读写。

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GitHub

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unknown

最后核验

2026-05-01

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请帮我安装这个 Agent Skill：bulk-rna-seq-deconvolution-with-bulk2single（使用 bulk2single 进行批量 rna seq 反卷积）
来源仓库：https://github.com/starlitnightly/omicverse
仓库路径：skills/bulk-rna-seq-deconvolution-with-bulk2single
安装命令：
npx skills add https://github.com/starlitnightly/omicverse --skill bulk-rna-seq-deconvolution-with-bulk2single
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

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复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

skills.shnpx skills

npx skills add https://github.com/starlitnightly/omicverse --skill bulk-rna-seq-deconvolution-with-bulk2single

简介

bulk-rna-seq-deconvolution-with-bulk2single 实现从 bulk RNA 测序重建单细胞 profile。

利用参考 scRNA-seq 图谱训练生成器，反卷积混合信号。
包含 PDAC 数据集演示，支持 beta-VAE 模型和性能评估。
要求用户提供 bulk 计数表和匹配的单细胞参考数据。
适用宿主包括 Codex、Claude、Cursor、Gemini CLI，接入前应确认版本、权限和运行环境要求。

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.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

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来源信息

GitHub 仓库

github.com

/starlitnightly/omicverse

SKILL.md 来源

github.com

/starlitnightly/omicverse/tree/main/skills/bulk-rna-seq-deconvolution-with-bulk2single

skills-sh

skills.sh

/starlitnightly/omicverse/bulk-rna-seq-deconvolution-with-bulk2single

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