Scvi-Hub: A Flexible Framework for Reference Enabled Single-Cell Data Analysis

Abstract

The accumulation of single-cell omics datasets in the public domain has opened new opportunities to reuse and leverage the vast amount of information they con- tain. Such uses, however, are complicated by the need for complex and resource- consuming procedures for data transfer, normalization, and integration that must be addressed prior to any analysis. Here we present scvi-hub: a platform for evalu- ating, sharing, and accessing probabilistic models that were trained on single-cell omics datasets. We demonstrate that these pre-trained models allow immediate access to a slew of fundamental tasks like visualization, imputation, annotation, outlier detection, and deconvolution of new (query) datasets with a much lower requirement for compute resources. We also show that pretrained models can help drive new discoveries with the existing (reference) datasets through rapid, model- based analyses. Scvi-hub is built within scvi-tools and integrated into scverse. Scvi-hub is publicly available to enable efficient sharing of single-cell omic stud- ies, and also to put advanced capabilities for transfer learning at the fingertips of a broad community of users. We provide an extended journal version on bioRxiv