bio-single-cell-multimodal-integration
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-multimodal-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.
## Version Compatibility Reference examples tested with: numpy 1.26+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Multimodal Integration **"Integrate RNA and protein data from my CITE-seq experiment"** → Jointly analyze multiple modalities (RNA + protein, RNA + ATAC) measured in the same cells using weighted nearest neighbor or factor analysis. - R: `Seurat::FindMultiModalNeighbors()` for WNN integration - Python: `muon` for MuData handling, `scanpy` + `anndata` for multimodal objects Analyze multi-modal single-cell data where multiple measurements are made per cell. ## Common Modalities | Technology | Modalities | Package | |------------|------------|---------| | CITE-seq | RNA + surface proteins (ADT) | Seurat | | 10X Multiome | RNA + ATAC | Seurat, Signac, ArchR | | SHARE-seq | RNA + ATAC | Seurat, Signac | | Spatial (Vi
- Version Compatibility
- Common Modalities
- CITE-seq Analysis (Seurat)
- Load Data
- QC and Normalization
- Weighted Nearest Neighbor (WNN) Clustering
- Visualize
- 10X Multiome (RNA + ATAC)
- Process ATAC
- Joint Analysis
- Scanpy/MuData (Python)
- CITE-seq with MuData
- Integration Metrics
- Modality Weights
What does the bio-single-cell-multimodal-integration skill do?
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-multimodal-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.