ArticlebioRxiv : the preprint server for biology2024
Scaling deep identifiable models enables zero-shot characterization of single-cell biological states.
Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
How to identify true biological differences across samples while overcoming batch effects has been a persistent challenge in single-cell RNA-seq data analysis, hindering analyses across datasets for transferable biological findings. In this work, we show that scaling up deep identifiable models leads to a surprisingly effective solution for this challenging task. We developed scShift, a deep variational inference framework with theoretical support in disentangling batch-dependent and independent variations. By training the model with compendiums of scRNA-seq atlases, scShift shows remarkable
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