scarches

What problem does it solve?

This Skill enables researchers to integrate and map new single-cell datasets into a curated reference atlas, enabling cross-study analyses, batch-aware embeddings, and consistent downstream interpretation.

Core Features & Use Cases

• Reference mapping and atlas surgery: align query datasets to an established atlas to transfer annotations and harmonize embeddings.
• Multi-omics and spatial integration: support scRNA-seq, scATAC-seq, CITE-seq, and spatial chromatin data through scArches workflows.
• Batch correction and cross-dataset analysis: robust integration of multiple studies, detection of novel cell states, and joint clustering.
• Use Cases: Build comprehensive reference atlases from multiple studies, map new datasets to existing atlases, and perform label transfer and comparative analysis across conditions.

Quick Start

Prepare reference and query AnnData objects, install scArches and dependencies, select a model (SCVI, TRVAE, scANVI, totalVI, mvTCR, or scPoli), train on the reference dataset, and perform surgery/mapping on the query dataset to obtain integrated embeddings and transferred annotations.