scvi-tools/tabula-sapiens-bladder-stereoscope

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

An SCVI model and minified AnnData of the Tahoe-100M dataset from Vevo Tx.

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.