References#
Ricard Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, Britta Velten, John C Marioni, and Oliver Stegle. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol., 21(1):111, May 2020. PMID:32393329, doi:10.1186/s13059-020-02015-1.
Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, and Oliver Stegle. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol., 14(6):e8124, 06 2018. PMID:29925568, doi:10.15252/msb.20178124.
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.
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, 112(518):859–877, 2017. URL: http://arxiv.org/abs/1601.00670v9; http://arxiv.org/pdf/1601.00670v9, arXiv:1601.00670v9, doi:10.1080/01621459.2017.1285773.
Tümay Capraz, Harald Vöhringer, Klaus Sebastian Augusto Kruger Serrano, Ricardo Omar Ramirez Flores, Julio Saez-Rodriguez, and Wolfgang Huber. Semi-supervised omics factor analysis (sofa) disentangles known and latent sources of variation in multi-omic data. bioRxiv, pages 2024–10, 2024.
Carlos M. Carvalho, Nicholas G. Polson, and James G. Scott. Handling sparsity via the horseshoe. In David van Dyk and Max Welling, editors, Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, volume 5 of Proceedings of Machine Learning Research, 73–80. Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA, apr 2009. PMLR. URL: https://proceedings.mlr.press/v5/carvalho09a.html.
Carlos M. Carvalho, Nicholas G. Polson, and James G. Scott. The horseshoe estimator for sparse signals. Biometrika, 97(2):465–480, 2010. URL: http://www.jstor.org/stable/25734098 (visited on 2023-03-17), doi:10.2307/25734098.
H Robert Frost, Zhigang Li, and Jason H Moore. Principal component gene set enrichment (PCGSE). BioData Min, 8:25, 2015. PMID:26300978, doi:10.1186/s13040-015-0059-z.
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. Nat Methods, 18(3):272–282, March 2021. PMID:33589839, doi:10.1038/s41592-020-01050-x.
Xiaoping Han, Renying Wang, Yincong Zhou, Lijiang Fei, Huiyu Sun, Shujing Lai, Assieh Saadatpour, Ziming Zhou, Haide Chen, Fang Ye, Daosheng Huang, Yang Xu, Wentao Huang, Mengmeng Jiang, Xinyi Jiang, Jie Mao, Yao Chen, Chenyu Lu, Jin Xie, Qun Fang, Yibin Wang, Rui Yue, Tiefeng Li, He Huang, Stuart H Orkin, Guo-Cheng Yuan, Ming Chen, and Guoji Guo. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell, 173(5):1307, May 2018. PMID:29775597, doi:10.1016/j.cell.2018.05.012.
Hyun Min Kang, Meena Subramaniam, Sasha Targ, Michelle Nguyen, Lenka Maliskova, Elizabeth McCarthy, Eunice Wan, Simon Wong, Lauren Byrnes, Cristina M Lanata, Rachel E Gate, Sara Mostafavi, Alexander Marson, Noah Zaitlen, Lindsey A Criswell, and Chun Jimmie Ye. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol, 36(1):89–94, January 2018. PMID:29227470, doi:10.1038/nbt.4042.
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. Automatic differentiation variational inference. Journal of Machine Learning Research, 18(14):1–45, 2017. URL: http://jmlr.org/papers/v18/16-107.html.
Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu, and Jianfeng Gao. Bridging Discrete and Backpropagation: Straight-Through and Beyond. April 2023. URL: http://arxiv.org/abs/2304.08612v1; http://arxiv.org/pdf/2304.08612v1, arXiv:2304.08612v1.
Juho Piironen and Aki Vehtari. Sparsity information and regularization in the horseshoe and other shrinkage priors. Electronic Journal of Statistics, 11(2):5018 – 5051, 2017. URL: https://doi.org/10.1214/17-EJS1337SI, doi:10.1214/17-EJS1337SI.
Nicholas G. Polson and James G. Scott. Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction. In Bayesian Statistics 9. Oxford University Press, 10 2011. URL: https://doi.org/10.1093/acprof:oso/9780199694587.003.0017, arXiv:https://academic.oup.com/book/0/chapter/141655378/chapter-ag-pdf/45229839/book\_1879\_section\_141655378.ag.pdf, doi:10.1093/acprof:oso/9780199694587.003.0017.
Arber Qoku and Florian Buettner. Encoding domain knowledge in multi-view latent variable models: a bayesian approach with structured sparsity. In Francisco Ruiz, Jennifer Dy, and Jan-Willem van de Meent, editors, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, 11545–11562. PMLR, 25–27 Apr 2023. URL: https://proceedings.mlr.press/v206/qoku23a.html.
Rajesh Ranganath, Sean Gerrish, and David Blei. Black Box Variational Inference. In Samuel Kaski and Jukka Corander, editors, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, volume 33 of Proceedings of Machine Learning Research, 814–822. Reykjavik, Iceland, 22–25 Apr 2014. PMLR. URL: https://proceedings.mlr.press/v33/ranganath14.html.
Daniel Ritchie, Paul Horsfall, and Noah D. Goodman. Deep Amortized Inference for Probabilistic Programs. October 2016. URL: http://arxiv.org/abs/1610.05735v1, arXiv:1610.05735v1.
F William Townes and Barbara E Engelhardt. Nonnegative spatial factorization applied to spatial genomics. Nat Methods, 20(2):229–238, February 2023. PMID:36587187, doi:10.1038/s41592-022-01687-w.
Britta Velten, Jana M Braunger, Ricard Argelaguet, Damien Arnol, Jakob Wirbel, Danila Bredikhin, Georg Zeller, and Oliver Stegle. Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat Methods, 19(2):179–186, 02 2022. PMID:35027765, doi:10.1038/s41592-021-01343-9.
Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Philipp Angerer, Volker Bergen, Pierre Boyeau, Maren Büttner, Gokcen Eraslan, David Fischer, Max Frank, Justin Hong, Michal Klein, Marius Lange, Romain Lopez, Mohammad Lotfollahi, Malte D. Luecken, Fidel Ramirez, Jeffrey Regier, Sergei Rybakov, Anna C. Schaar, Valeh Valiollah Pour Amiri, Philipp Weiler, Galen Xing, Bonnie Berger, Dana Pe'er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle, and Fabian J. Theis and. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology, apr 2023. URL: https://doi.org/10.1038%2Fs41587-023-01733-8, doi:10.1038/s41587-023-01733-8.
David Wingate and Theophane Weber. Automated Variational Inference in Probabilistic Programming. January 2013. URL: http://arxiv.org/abs/1301.1299v1, arXiv:1301.1299v1.
Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Qian, and John L. Hopper. Bayesian Sparse Global-Local Shrinkage Regression for Selection of Grouped Variables. November 2017. URL: http://arxiv.org/abs/1709.04333v3; http://arxiv.org/pdf/1709.04333v3, arXiv:1709.04333v3.