scBIOT

scBIOT is a lightweight Python library for single-cell omics integration. It bundles the preprocessing, embedding, transfer label workflows we routinely apply to RNA, ATAC, and paired or unpaired multi-omics datasets. The library emphasizes reproducible data preparation, single-cell clustering using embeddings derived from optimal transport, and concise APIs that work out of the box on AnnData data.

Highlights

• Batteries-included preprocessing: scATAC-seq peak processing, iterative LSI, and gene activity annotation.

• Accurate atlas integration: high-fidelity alignment with rare cell-type protection.

• Unified scBIOT framework: a single framework for embedding RNA, ATAC, transfer learning, and paired or unpaired multi-omics.

• Fast integration via Optimal Transport (OT): scalable alignment for large single-cell datasets.

• Scales to 100M cells locally: memory-efficent scalable processing.

• Label transfer: across multi-omics modalities and between spatial data and scRNA-seq references.

Getting started

Install the package (with optional extras) and launch Jupyter from the repository root:

pip install scbiot

Workflows

• 1. scRNA-seq
• 2. scRNA-seq in R (Seurat + reticulate)
• 3. snATAC-seq
• 4. Paired Multiomics
• 5. Unpaired Multiomics Integration
• 6. Centroid-Level Optimal Transport
• 7. brain_1.3M_integration
• 8. Label transfer with supBIOT