# References

See [references.bib](references.bib) for the full BibTeX bibliography.

Key citations for scvi-tools models covered by this server:

## Core RNA Models

- **scVI**: Lopez et al., *Deep generative modeling for single-cell transcriptomics*, Nature Methods 2018
- **scANVI**: Xu et al., *Probabilistic harmonization and annotation of single-cell transcriptomics data*, Molecular Systems Biology 2021
- **LinearSCVI**: Svensson et al., *Interpretable factor models of single-cell RNA-seq via variational autoencoders*, Bioinformatics 2020
- **AUTOZI**: Clivio et al., *Detecting zero-inflated genes in single-cell transcriptomics data*, MLCB 2019
- **AmortizedLDA**: Blei, Ng, Jordan, *Latent Dirichlet Allocation*, JMLR 2003

## Multimodal / CITE-seq Models

- **TotalVI**: Gayoso et al., *Joint probabilistic modeling of single-cell multi-omic data with totalVI*, Nature Methods 2021
- **MultiVI**: Ashuach et al., *MultiVI: deep generative model for the integration of multimodal data*, Nature Methods 2023
- **DiagVI**: inspired by GLUE — Cao & Gao, *Multi-omics single-cell data integration*, Nature Biotechnology 2022
- **CytoVI**: Ingelfinger et al., *CytoVI: Deep generative modeling of antibody-based single cell technologies*, bioRxiv 2025

## ATAC / Chromatin Accessibility Models

- **PeakVI**: Ashuach et al., *PeakVI: a deep generative model for single-cell chromatin accessibility analysis*, Cell Reports Methods 2022
- **SCBASSET**: Yuan & Kelley, *scBasset: sequence-based modeling of single-cell ATAC-seq*, Nature Methods 2022
- **PoissonVI**: See scvi-tools documentation

## Velocity Models

- **veloVI**: Gayoso et al., *Deep generative modeling of transcriptional dynamics for RNA velocity analysis*, Nature Methods 2023

## Multi-sample / Batch Analysis

- **MrVI**: Boyeau et al., *Deep generative modeling of sample-level heterogeneity in single-cell genomics*, bioRxiv 2023
- **ContrastiveVI**: Weinberger et al., *Isolating salient variations of interest in single-cell data with contrastiveVI*, Genome Biology 2023
- **SysVI**: Hrovatin et al., *Integrating single-cell RNA-seq datasets with substantial batch effects*, bioRxiv 2023

## Doublet Detection

- **SOLO**: Bernstein et al., *Solo: doublet identification in single-cell RNA-seq via semi-supervised deep learning*, Cell Systems 2020

## Cell Type Annotation

- **CellAssign**: Zhang et al., *Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling*, Nature Methods 2019

## Spatial Transcriptomics Models

- **DestVI**: Lopez et al., *DestVI identifies continuums of cell types in spatial transcriptomics data*, Nature Biotechnology 2022
- **Stereoscope**: Andersson et al., *Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography*, Communications Biology 2020
- **Tangram**: Biancalani et al., *Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram*, Nature Methods 2021
- **ResolVI**: Ergen & Yosef, *ResolVI - addressing noise and bias in spatial transcriptomics*, bioRxiv 2025
- **scVIVA**: See scvi-tools documentation for `scvi.external.SCVIVA`
- **Decipher**: See scvi-tools documentation for `scvi.external.Decipher`

## Methylation Models

- **MethylVI / MethylANVI**: Weinberger & Lee, *A deep generative model of single-cell methylomic data*, OpenReview 2021

## Ambient RNA Removal

- **scAR**: Sheng et al., *Probabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omics*, bioRxiv 2022

## Transfer Learning

- **scArches**: Lotfollahi et al., *Mapping single-cell data to reference atlases by transfer learning*, Nature Biotechnology 2021

## Background Methods

- **Variational Inference / VAE**: Kingma & Welling, *An introduction to variational autoencoders*, arXiv 2019; Blei et al., *Variational inference: A review for statisticians*, JASA 2017