References

References#

BibTeX entries are in references.bib.

Core Models#

scVIVA#

Levy et al. (2025) scVIVA: a probabilistic framework for representation of cells and their environments in spatial transcriptomics bioRxiv. doi: 10.1101/2025.06.01.657182 cite:Levy25

scVIVA models gene expression as a function of both the cell’s intrinsic state and its microenvironment (niche). Niche-conditioned decoders disentangle cell-intrinsic from environment-driven variation, enabling niche-aware differential expression.

DestVI#

Lopez et al. (2022) DestVI identifies continuums of cell types in spatial transcriptomics data Nature Biotechnology. doi: 10.1038/s41587-022-01272-8 cite:Lopez22

DestVI performs multi-resolution deconvolution of spatial transcriptomics spots into cell-type compositions using a conditional SCVI reference trained on scRNA-seq.

ResolVI#

Ergen & Yosef (2025) ResolVI - addressing noise and bias in spatial transcriptomics bioRxiv. doi: 10.1101/2025.01.20.634005 cite:Ergen25

ResolVI corrects segmentation errors, background signal, and cell-size bias in cellular-resolution spatial transcriptomics (Xenium, MERFISH, CosMx) using a Pyro-based probabilistic model with neighbor-aware decoders.

Foundation: scvi-tools#

Gayoso et al. (2022) A Python library for probabilistic analysis of single-cell omics data Nature Biotechnology, 40, 163–166. doi: 10.1038/s41587-021-01206-w cite:Gayoso22

Lopez et al. (2018) Deep generative modeling for single-cell transcriptomics Nature Methods, 15, 1053–1058. doi: 10.1038/s41592-018-0229-2 cite:Lopez18

scverse Ecosystem#

Virshup et al. (2024) — The scverse project Nature Biotechnology, 42, 333–336. doi: 10.1038/s41587-023-01733-8 cite:Virshup24

Palla et al. (2022) — squidpy Nature Methods, 19, 171–178. doi: 10.1038/s41592-021-01358-2 cite:Palla22

Wolf et al. (2018) — scanpy Genome Biology, 19, 15. doi: 10.1186/s13059-017-1382-0 cite:Wolf18

Deep Learning Frameworks#

Paszke et al. (2019) — PyTorch. NeurIPS 2019. cite:Paszke19

Falcon et al. (2019) — PyTorch Lightning. cite:Falcon19

Bingham et al. (2019) — Pyro. JMLR, 20(28). cite:Bingham19

GPU Acceleration#

NVIDIA Corporation — RAPIDS (cuML / cuGraph). https://rapids.ai cite:RAPIDS

Future External Models#

Model	Reference	Key
Stereoscope	Andersson et al. (2020), Commun Biol	`cite:Andersson20`
Tangram	Biancalani et al. (2021), Nature Methods	`cite:Biancalani21`
Cell2location	Kleshchevnikov et al. (2022), Nature Biotechnology	`cite:Kleshchevnikov22`
starfysh	Chang et al. (2023) — BibTeX entry TBD	—
VIVS / SPARL / others	References TBD	—