User

Import scvi-tools as:

import scvi

Model

model.AUTOZI	Automatic identification of ZI genes [Clivio19].
model.CondSCVI	Conditional version of single-cell Variational Inference, used for multi-resolution deconvolution of spatial transcriptomics data [Lopez21].
model.DestVI	Multi-resolution deconvolution of Spatial Transcriptomics data (DestVI) [Lopez21].
model.LinearSCVI	Linearly-decoded VAE [Svensson20].
model.PEAKVI	Peak Variational Inference [Ashuach22]
model.SCANVI	Single-cell annotation using variational inference [Xu21].
model.SCVI	single-cell Variational Inference [Lopez18].
model.TOTALVI	total Variational Inference [GayosoSteier21].
model.MULTIVI	Integration of multi-modal and single-modality data [AshuachGabitto21].
model.AmortizedLDA	Amortized Latent Dirichlet Allocation [Blei03].
model.JaxSCVI	EXPERIMENTAL single-cell Variational Inference [Lopez18], but with a Jax backend.

External models

external.CellAssign	Reimplementation of CellAssign for reference-based annotation [Zhang19].
external.GIMVI	Joint VAE for imputing missing genes in spatial data [Lopez19].
external.RNAStereoscope	Reimplementation of Stereoscope [Andersson20] for deconvolution of spatial transcriptomics from single-cell transcriptomics.
external.SpatialStereoscope	Reimplementation of Stereoscope [Andersson20] for deconvolution of spatial transcriptomics from single-cell transcriptomics.
external.SOLO	Doublet detection in scRNA-seq [Bernstein19].

Data loading

scvi-tools relies entirely on the AnnData format. For convenience, we have included data loaders from the AnnData API. Scanpy also has utilities to load data that are outputted by 10x’s Cell Ranger software.

data.read_h5ad	Read .h5ad-formatted hdf5 file.
data.read_csv	Read .csv file.
data.read_loom	Read .loom-formatted hdf5 file.
data.read_text	Read .txt, .tab, .data (text) file.
data.read_10x_atac	Read scATAC-seq data outputted by 10x Genomics software.
data.read_10x_multiome	Read Multiome (scRNA + scATAC) data outputted by 10x Genomics software.

Basic preprocessing

For general single-cell preprocessing, we defer to our friends at Scanpy, and specifically their preprocessing module (scanpy.pp).

All scvi-tools models require raw UMI count data. The count data can be safely stored in an AnnData layer as one of the first steps of a Scanpy single-cell workflow:

adata.layers["counts"] = adata.X.copy()

Here we maintain a few package specific utilities for feature selection, etc.

data.poisson_gene_selection	Rank and select genes based on the enrichment of zero counts.
data.organize_cite_seq_10x	Organize anndata object loaded from 10x for scvi models.
data.organize_multiome_anndatas	Concatenate multiome and single-modality input anndata objects.

Utilities

Here we maintain miscellaneous general methods.

model.utils.mde

Util to run pymde.preserve_neighbors() for visualization of scvi-tools embeddings.

Configuration

An instance of the ScviConfig is available as scvi.settings and allows configuring scvi-tools.

_settings.ScviConfig

Config manager for scvi-tools.