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 [Levy et al., 2025]
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 [Lopez et al., 2022]
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 [Ergen and Yosef, 2025]
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.
Harreman#
Etxezarreta Arrastoa et al. (2025) Metabolic zonation and characterization of tissue slices with spatial transcriptomics bioRxiv. doi: 10.1101/2025.11.11.687271 [Arrastoa et al., 2025]
Harreman infers spatially-resolved metabolic gene programs and cell-cell metabolic/ligand-receptor communication from spatial transcriptomics data using local autocorrelation and spatial proximity graphs.
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 [Gayoso et al., 2022]
Lopez et al. (2018) Deep generative modeling for single-cell transcriptomics Nature Methods, 15, 1053–1058. doi: 10.1038/s41592-018-0229-2 [Lopez et al., 2018]
scverse Ecosystem#
Virshup et al. (2024) — The scverse project Nature Biotechnology, 42, 333–336. doi: 10.1038/s41587-023-01733-8 [Virshup et al., 2024]
Palla et al. (2022) — squidpy Nature Methods, 19, 171–178. doi: 10.1038/s41592-021-01358-2 [Palla et al., 2022]
Wolf et al. (2018) — scanpy Genome Biology, 19, 15. doi: 10.1186/s13059-017-1382-0 [Wolf et al., 2018]
Deep Learning Frameworks#
Paszke et al. (2019) — PyTorch. NeurIPS 2019. [Paszke et al., 2019]
Falcon et al. (2019) — PyTorch Lightning. [Falcon and The PyTorch Lightning Team, 2019]
Bingham et al. (2019) — Pyro. JMLR, 20(28). [Bingham et al., 2019]
GPU Acceleration#
NVIDIA Corporation — RAPIDS (cuML / cuGraph). https://rapids.ai [NVIDIA Corporation, 2018]
Future External Models#
Model |
Reference |
Key |
|---|---|---|
Stereoscope |
Andersson et al. (2020), Commun Biol |
|
Tangram |
Biancalani et al. (2021), Nature Methods |
|
Cell2location |
Kleshchevnikov et al. (2022), Nature Biotechnology |
|
starfysh |
Chang et al. (2023) — BibTeX entry TBD |
— |
VIVS / SPARL / others |
References TBD |
— |
Bibliography#
Alma Andersson, Joseph Bergenstråhle, Michaela Asp, Ludvig Bergenstråhle, Aleksandra Jurek, José Fernández Navarro, and Joakim Lundeberg. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Communications Biology, October 2020. doi:10.1038/s42003-020-01247-y.
Oier Etxezarreta Arrastoa, Anna-Chiara Pirona, Allon Wagner, and Nir Yosef. Metabolic zonation and characterization of tissue slices with spatial transcriptomics. biorxiv, November 2025. doi:10.1101/2025.11.11.687271.
Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Nerim Tokcan, Charles R. Vanderburg, Åsa Segerstolpe, Meng Zhang, Inbal Avraham-Davidi, Sanja Vickovic, Mor Nitzan, Sai Ma, Ayshwarya Subramanian, Michal Lipinski, Jason Buenrostro, Nik Bear Brown, Duccio Fanelli, Xiaowei Zhuang, Evan Z. Macosko, and Aviv Regev. Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram. Nature Methods, 18(11):1352–1362, 2021.
Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D. Goodman. Pyro: deep universal probabilistic programming. Journal of Machine Learning Research, 20(28):1–6, 2019. URL: http://jmlr.org/papers/v20/18-403.html.
Can Ergen and Nir Yosef. Resolvi - addressing noise and bias in spatial transcriptomics. biorxiv, January 2025. doi:10.1101/2025.01.20.634005.
William Falcon and The PyTorch Lightning Team. PyTorch Lightning. 2019. GitHub repository. URL: Lightning-AI/pytorch-lightning.
Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran, Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto, Mohammad Lotfollahi, Valentine Svensson, Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov, Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan, Jeffrey Regier, and Nir Yosef. A python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 40(2):163–166, February 2022. doi:10.1038/s41587-021-01206-w.
Vitalii Kleshchevnikov, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W. King, Tong Li, Artem Lomakin, Malte D. Luecken, Alexander T. Dilthey, Gord Fishell, Signe De Schepper, Niklas J. Savill, Mohammad Lotfollahi, Ivo G. Gut, Oliver Stegle, Omer Ali Bayraktar, and Fabian J. Theis. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology, 40:661–671, 2022. doi:10.1038/s41587-021-01139-4.
Nathan Levy, Florian Ingelfinger, Artemii Bakulin, Giacomo Cinnirella, Pierre Boyeau, Boaz Nadler, Can Ergen, and Nir Yosef. Scviva: a probabilistic framework for representation of cells and their environments in spatial transcriptomics. biorxiv, June 2025. doi:10.1101/2025.06.01.657182.
Romain Lopez, Baoguo Li, Hadas Keren-Shaul, Pierre Boyeau, Merav Kedmi, David Pilzer, Adam Jelinski, Ido Yofe, Eyal David, Allon Wagner, Yoseph Addadi, Ofra Golani, Franca Ronchese, Michael I. Jordan, Ido Amit, and Nir Yosef. Destvi identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology, April 2022. doi:10.1038/s41587-022-01272-8.
Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, and Nir Yosef. A joint model of unpaired data from scrna-seq and spatial transcriptomics for imputing missing gene expression measurements. ICML Workshop on Computational Biology, 2019. doi:10.48550/ARXIV.1905.02269.
Romain Lopez, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, and Nir Yosef. Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12):1053–1058, November 2018. doi:10.1038/s41592-018-0229-2.
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D. Luecken, Matin Khajavi, Maren Büttner, Marco Wagenstetter, Žiga Avsec, Adam Gayoso, Nir Yosef, Marta Interlandi, Sergei Rybakov, Alexander V. Misharin, and Fabian J. Theis. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology, 40(1):121–130, August 2021. doi:10.1038/s41587-021-01001-7.
Giovanni Palla, Hannah Spitzer, Michal Klein, David Fischer, Anna Christina Schaar, Louis Benedikt Kuemmerle, Sergei Rybakov, Ignacio Leonardo Ibarra, Olle Holmberg, Isaac Virshup, Mohammad Lotfollahi, Sabrina Richter, and Fabian J. Theis. Squidpy: a scalable framework for spatial omics analysis. Nature Methods, 19:171–178, 2022. doi:10.1038/s41592-021-01358-2.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. PyTorch: an imperative style, high-performance deep gradient computing library. In Advances in Neural Information Processing Systems, volume 32. 2019.
Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Philipp Angerer, Volker Bergen, Pierre Boyeau, Maren Branchetti, and others. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology, 42:333–336, 2024. doi:10.1038/s41587-023-01733-8.
F. Alexander Wolf, Philipp Angerer, and Fabian J. Theis. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19:15, 2018. doi:10.1186/s13059-017-1382-0.
NVIDIA Corporation. RAPIDS: gpu data science. 2018. cuML (GPU machine learning) and cuGraph (GPU graph analytics). URL: https://rapids.ai.