References

References#

[ABA+20]

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.

[AGK+23]

Tal Ashuach, Mariano I. Gabitto, Rohan V. Koodli, Giuseppe-Antonio Saldi, Michael I. Jordan, and Nir Yosef. Multivi: deep generative model for the integration of multimodal data. Nature Methods, June 2023. doi:10.1038/s41592-023-01909-9.

[ARGY22]

Tal Ashuach, Daniel A. Reidenbach, Adam Gayoso, and Nir Yosef. Peakvi: a deep generative model for single-cell chromatin accessibility analysis. Cell Reports Methods, 2(3):100182, 2022. doi:10.1016/j.crmeth.2022.100182.

[BFL+20]

Nicholas J. Bernstein, Nicole L. Fong, Irene Lam, Margaret A. Roy, David G. Hendrickson, and David R. Kelley. Solo: doublet identification in single-cell RNA-seq via semi-supervised deep learning. Cell Systems, 11(1):95–101.e5, July 2020. doi:10.1016/j.cels.2020.05.010.

[BSB+21]

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.

[BNJ03]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, March 2003.

[BLR+19]

Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef. Deep generative models for detecting differential expression in single cells. Machine Learning in Computational Biology, October 2019. doi:10.1101/794289.

[CLR+19]

Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef. Detecting zero-inflated genes in single-cell transcriptomics data. Machine Learning in Computational Biology, October 2019. doi:10.1101/794875.

[GSL+21]

Adam Gayoso, Zoë Steier, Romain Lopez, Jeffrey Regier, Kristopher L. Nazor, Aaron Streets, and Nir Yosef. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods, 18(3):272–282, February 2021. doi:10.1038/s41592-020-01050-x.

[LLKS+22]

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.

[LNL+19]

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.

[LRC+18]

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.

[LNL+21]

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.

[LRA+19]

Aaron T. L. Lun, Samantha Riesenfeld, Tallulah Andrews, The Phuong Dao, Tomas Gomes, and John C. Marioni. Emptydrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biology, March 2019. doi:10.1186/s13059-019-1662-y.

[MFY+23]

Laura D. Martens, David S. Fischer, Vicente A. Yépez, Fabian J. Theis, and Julien Gagneur. Modeling fragment counts improves single-cell ATAC-seq analysis. Nature Methods, December 2023. doi:10.1038/s41592-023-02112-6.

[SLL+22]

Caibin Sheng, Rui Lopes, Gang Li, Sven Schuierer, Annick Waldt, Rachel Cuttat, Slavica Dimitrieva, Audrey Kauffmann, Eric Durand, Giorgio G. Galli, Guglielmo Roma, and Antoine de Weck. Probabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omics. bioRxiv, January 2022. doi:10.1101/2022.01.14.476312.

[SGYP20]

Valentine Svensson, Adam Gayoso, Nir Yosef, and Lior Pachter. Interpretable factor models of single-cell RNA-seq via variational autoencoders. Bioinformatics, 36(11):3418–3421, March 2020. doi:10.1093/bioinformatics/btaa169.

[WLL23]

Ethan Weinberger, Chris Lin, and Su-In Lee. Isolating salient variations of interest in single-cell data with contrastivevi. Nature Methods, 20(9):1336–1345, August 2023. doi:10.1038/s41592-023-01955-3.

[XLM+21]

Chenling Xu, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I. Jordan, and Nir Yosef. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Molecular Systems Biology, January 2021. doi:10.15252/msb.20209620.

[YK22]

Han Yuan and David R. Kelley. Scbasset: sequence-based modeling of single-cell atac-seq using convolutional neural networks. Nature Methods, 19(9):1088–1096, 2022.

[ZOFlanaganC+19]

Allen W. Zhang, Ciara O'Flanagan, Elizabeth A. Chavez, Jamie L. P. Lim, Nicholas Ceglia, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N. McAlpine, Samuel Aparicio, Christian Steidl, Kieran R. Campbell, and Sohrab P. Shah. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nature Methods, 16(10):1007–1015, September 2019. doi:10.1038/s41592-019-0529-1.