bio-machine-learning-atlas-mapping
Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
npx skills add BioTender-max/awesome-bio-agent-skills --skill atlas-mapping --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: anndata 0.10+, scanpy 1.10+, scvi-tools 1.1+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Transfer Learning for Single-Cell Data **"Map my scRNA-seq data onto a reference atlas"** → Transfer cell type labels from a pre-trained reference model to query cells using architectural surgery (scArches) without retraining. - Python: `scvi.model.SCVI.load_query_data()` → `get_latent_representation()` → `scanpy.tl.ingest()` ## scVI Reference Mapping (scArches) **Goal:** Map query single-cell data onto a pre-trained reference model to obtain a shared latent embedding. **Approach:** Load a pre-trained scVI model, prepare query data with matching gene sets, then perform surgical fine-tuning that updates only query-specific parameters. ```python import scvi import scanpy as sc # Load pre-trained reference model adata_ref = sc.read_h5ad('reference.h5ad') # Model must h
- Version Compatibility
- scVI Reference Mapping (scArches)
- scANVI for Label Transfer
- Prediction Confidence
- Joint Embedding Visualization
- Pre-trained Reference Atlases
- Training Parameters
- Related Skills
What does the bio-machine-learning-atlas-mapping skill do?
Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill atlas-mapping --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.