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Integrate multi-sample scRNA-seq data with Harmony, scVI, scANVI, BBKNN, Scanorama, or supported R-backed integration methods.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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| name | sc-batch-integration |
| description | Integrate multi-sample scRNA-seq data with Harmony, scVI, scANVI, BBKNN, Scanorama, or supported R-backed integration methods. |
| version | 0.5.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["singlecell","batch-integration","harmony","scvi","scanorama","bbknn"] |
| metadata | {"omicsclaw":{"domain":"singlecell","allowed_extra_flags":["--batch-key","--method","--n-epochs","--no-gpu","--n-latent","--labels-key","--harmony-theta","--bbknn-neighbors-within-batch","--scanorama-knn","--integration-features","--integration-pcs","--simba-n-top-genes","--simba-n-components","--simba-k","--simba-num-workers","--r-enhanced"],"param_hints":{"harmony":"[Truncated]","scvi":"[Truncated]","scanvi":"[Truncated]","bbknn":"[Truncated]","scanorama":"[Truncated]","fastmnn":"[Truncated]","seurat_cca":"[Truncated]","seurat_rpca":"[Truncated]"},"legacy_aliases":["sc-integrate"],"saves_h5ad":true,"requires_preprocessed":true,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"S","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["batch integration","batch effect","harmony","scvi","bbknn","merge samples"]}} |
batch_key-centric interface across Python and R paths.figure_data/ CSVs plus a gallery manifest for downstream restyling.processed.h5ad, tables, report, structured result JSON, README, and notebook bundle.Actively implemented methods in this wrapper:
harmonyscviscanvibbknnscanoramaGraph-embedding methods (require additional packages):
simba — SIMBA graph-embedding integration via PBG (requires simba package)R-backed methods (require corresponding R packages):
fastmnnseurat_ccaseurat_rpca| Format | Extension / form | Current wrapper support | Notes |
|---|---|---|---|
| AnnData | .h5ad | yes | current direct input path |
| Demo | --demo | yes | bundled merged-example fallback |
batch, sample, or sample_id.harmony, bbknn, and scanorama operate on normalized / PCA-ready representations.scvi, scanvi, fastmnn, seurat_cca, and seurat_rpca should preserve raw counts in layers["counts"] when available.sc-clustering as the next standard step.processed.h5ad, report.md, and result.json.python skills/singlecell/scrna/sc-batch-integration/sc_integrate.py \
--input <merged.h5ad> --method harmony --batch-key sample_id --output <dir>
python skills/singlecell/scrna/sc-batch-integration/sc_integrate.py \
--input <merged.h5ad> --method scvi --batch-key sample_id \
--n-epochs 400 --output <dir>
python skills/singlecell/scrna/sc-batch-integration/sc_integrate.py \
--input <merged.h5ad> --method scanorama --batch-key sample_id --output <dir>
| Parameter | Role | Notes |
|---|---|---|
--method | integration backend | harmony, scvi, scanvi, bbknn, scanorama, fastmnn, seurat_cca, or seurat_rpca |
--batch-key | batch metadata column | core public control across all backends |
--n-epochs | training/runtime control | used by scvi and scanvi |
--n-latent | latent dimension | used by scvi and scanvi |
--labels-key | label column | used by scanvi |
--no-gpu | force CPU execution | relevant to scvi / scanvi paths |
--harmony-theta | Harmony diversity penalty | used by harmony |
--bbknn-neighbors-within-batch | local neighbors per batch | used by bbknn |
--scanorama-knn | panorama neighbor count | used by scanorama |
--integration-features | integration features | used by fastmnn, seurat_cca, seurat_rpca |
--integration-pcs | integration PCs | used by harmony, fastmnn, seurat_cca, seurat_rpca |
--simba-n-top-genes | variable genes per batch | used by simba (default 3000) |
--simba-n-components | components for edge inference | used by simba (default 15) |
--simba-k | neighbors for edge inference | used by simba (default 15) |
--simba-num-workers | PBG training workers | used by simba (default 4) |
Current OmicsClaw sc-batch-integration always:
batch_key existsharmony, bbknn, scanoramascvi, scanvifastmnn, seurat_cca, seurat_rpcaImportant implementation notes:
scanvi depends on labels; the demo path now injects stable labels, but real inputs still need cell_type, leiden, louvain, or another trustworthy label column.Successful runs write:
processed.h5adreport.mdresult.jsonfigures/manifest.jsonfigure_data/manifest.jsontables/integration_summary.csvtables/batch_sizes.csvreproducibility/commands.shThe current standard Python gallery is recipe-based and uses:
overview: UMAP colored by batch and, when a stable label column already exists, by that label columndiagnostic: batch-mixing heatmap when stable labels already existsupporting: integration metric summary bar plotfigure_data/ is the stable hand-off layer for downstream styling without rerunning integration.
report.mdfigures/umap_<batch_key>.pngfigures/batch_mixing_heatmap.pngtables/integration_summary.csvprocessed.h5adsc-clustering.report.md / result.json, for example:
sc-clustering --use-rep X_harmonysc-clustering --use-rep X_scvisc-clustering --use-rep X_scanoramasc-preprocessing -> sc-clustering path.fastmnn, seurat_cca, and seurat_rpca require a working R environment with batchelor or Seurat plus the H5AD bridge packages.README.md and notebook-style reproducibility artifacts when notebook export dependencies are available.Upstream: sc-preprocessing (when multiple batches are present) Downstream: sc-clustering
| Flag | Type | Default | Description | Validation |
|---|---|---|---|---|
--input | path | — | Input AnnData file; required unless --demo | — |
--output | path | — | Output directory (required) | — |
--demo | flag | false | Run with built-in demo data | — |
--method | enum | harmony | Integration backend: harmony, scvi, scanvi, bbknn, scanorama, simba, fastmnn, seurat_cca, seurat_rpca | — |
--batch-key | str | batch | Obs column containing batch/sample labels | — |
--n-epochs | int | none | Training epochs (scvi, scanvi; defaults: scvi=400, scanvi=200) | — |
--no-gpu | flag | false | Force CPU execution for scvi/scanvi | — |
--n-latent | int | none | Latent space dimension (scvi, scanvi; default: 30) | — |
--labels-key | str | none | Obs column with cell type labels (scanvi only) | — |
--harmony-theta | float | none | Harmony diversity penalty (harmony; default: 2.0) | — |
--bbknn-neighbors-within-batch | int | none | Neighbors per batch for BBKNN (bbknn; default: 3) | — |
--scanorama-knn | int | none | Panorama neighbor count (scanorama; default: 20) | — |
--integration-features | int | none | Number of integration features (fastmnn, seurat_cca, seurat_rpca; default: 2000) | — |
--integration-pcs | int | none | Integration PCs (harmony, fastmnn, seurat_cca, seurat_rpca; default: 50/30) | — |
--simba-n-top-genes | int | none | Variable genes per batch for SIMBA (simba; default: 3000) | — |
--simba-n-components | int | none | Components for SIMBA edge inference (simba; default: 15) | — |
--simba-k | int | none | Neighbors for SIMBA edge inference (simba; default: 15) | — |
--simba-num-workers | int | none | PBG training workers for SIMBA (simba; default: 4) | — |
--r-enhanced | flag | false | Generate R Enhanced figures via ggplot2 renderers | — |
| Renderer | Output file | What it shows | R packages |
|---|---|---|---|
plot_embedding_discrete | r_embedding_discrete.png | Cell embedding scatter colored by batch/cluster labels post-integration | ggplot2, ggrepel, cowplot |
plot_embedding_feature | r_embedding_feature.png | Cell embedding scatter with continuous feature expression overlay | ggplot2, viridis, cowplot |
plot_cell_barplot | r_cell_barplot.png | Cell count bar chart per batch/sample | ggplot2, cowplot |
plot_cell_sankey | r_cell_sankey.png | Sankey diagram of cell type transitions across batches (CellStatPlot sankey equivalent) | ggplot2, ggalluvial, cowplot |