References¶
See 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.SCVIVADecipher: 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