bioRxivBioinformaticsPreprint
CytoVerse: Single-Cell AI Foundation Models in the Browser
Robert CurrieiD, Jesus Gonzalez-FerreriD, Mohammed A. Mostajo-RadjiiD, David H. HaussleriD
Abstract
Mapping single-cell datasets to large atlases is often hindered by server constraints and privacy concerns. We present CytoVerse, a framework that runs scRNA-seq Foundation Models (scFM) entirely in the browser. Three key contributions enable this: (1) deploying models via ONNX without server side compute; (2) using compressed indexing (IVFPQ) to search a more then 20 million cell reference from the client; and (3) a lightweight protocol for sharing embeddings across consortia without exposing raw data. CytoVerse thereby provides a scalable, privacy preserving framework for distributed single-cell analysis.
- assay: scRNA-seq
- research focus: benchmarking
- research focus: software / tool
- approach: deep learning
- approach: statistical model
- Citations
- 0
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