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Press

Core model publications​

  • scVIVA: a probabilistic framework for representation of cells and their environments in spatial transcriptomics. N. Levy, F. Ingelfinger, A. Bakulin, G. Cinnirella, P. Boyeau, B. Nadler, C. Ergen, N. Yosef. bioRxiv, 2025. doi: 10.1101/2025.06.01.657182

  • DestVI identifies continuums of cell types in spatial transcriptomics data. R. Lopez*, B. Li*, H. Keren-Shaul*, P. Boyeau, M. Kedmi, D. Pilzer, A. Jelinski, I. Yofe, E. David, A. Wagner, Y. Addadi, O. Golani, F. Ronchese, M.I. Jordan, I. Amit†, N. Yosef†. Nature Biotechnology, 2022. doi: 10.1038/s41587-022-01272-8

  • ResolVI: addressing noise and bias in spatial transcriptomics. C. Ergen, N. Yosef. bioRxiv, 2025. doi: 10.1101/2025.01.20.634005

  • Harreman: metabolic zonation and characterization of tissue slices with spatial transcriptomics. Etxezarreta Arrastoa et al. bioRxiv, 2025. doi: 10.1101/2025.11.11.687271

  • gimVI: a joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. R. Lopez*, A. Nazaret*, M. Langevin*, J. Samaran*, J. Regier*, M.I. Jordan, N. Yosef. ICML Workshop on Computational Biology, 2019.

  • Stereoscope: probabilistic cell-type deconvolution of spatial transcriptomics spots. Andersson et al. Communications Biology, 2020.

  • Tangram: deep learning-based mapping of single-cell RNA-seq data onto spatial coordinates. T. Biancalani et al. Nature Methods, 2021.

  • DiagVI: no dedicated scviva-tools publication yet (newest model, under active development). Builds on optimal-transport correspondence methods: Cao & Gao (2022); Séjourné, Feydy, Vialard, Trouvé, Peyré (2023).

Foundation​

  • A Python library for probabilistic analysis of single-cell omics data. A. Gayoso et al. Nature Biotechnology, 2022. doi: 10.1038/s41587-021-01206-w