bio-single-cell-batch-integration
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
npx skills add majiayu000/claude-skill-registry --skill batch-integration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Batch Integration Integrate multiple scRNA-seq datasets to remove batch effects while preserving biological variation. ## Tool Comparison | Tool | Speed | Scalability | Best For | |------|-------|-------------|----------| | Harmony | Fast | Good | Quick integration, most use cases | | scVI | Moderate | Excellent | Large datasets, deep learning | | Seurat CCA/RPCA | Moderate | Good | Conserved biology across batches | | fastMNN | Fast | Good | MNN-based correction | ## Harmony (R/Python) ### R with Seurat ```r library(Seurat) library(harmony) # Merge datasets first merged <- merge(sample1, y = list(sample2, sample3), add.cell.ids = c('S1', 'S2', 'S3')) # Standard preprocessing merged <- NormalizeData(merged) merged <- FindVariableFeatures(merged) merged <- ScaleData(merged) merged <- RunPCA(merged) # Run Harmony on PCA embeddings merged <- RunHarmony(merged, group.by.vars = 'orig.ident', dims.use = 1:30) # Use harmony embeddings for downstream merged <- RunUMAP(merged, reduction = 'harmony', dims = 1:30) merged <- FindNeighbors(merged, reduction = 'harmony', dims = 1:30) merged <- FindClusters(merged, resolution = 0.5) ``` ### Multiple Batch Variables ```r # Correct for both sampl
- Tool Comparison
- Harmony (R/Python)
- R with Seurat
- Multiple Batch Variables
- Python with Scanpy
- scVI (Python)
- scVI with Covariates
- scANVI (with cell type labels)
- Seurat Integration (R)
- CCA-based Integration
- RPCA (Faster for Large Datasets)
- Seurat v5 Integration
- fastMNN (R)
- Evaluate Integration
What does the bio-single-cell-batch-integration skill do?
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill batch-integration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.