References

References#

[ASJ+25]

Amir Akbarnejad, Lloyd Steele, Daniyal J Jafree, Sebastian Birk, Marta Rosa Sallese, Koen Rademaker, Adam Boxall, Benjamin Rumney, Catherine Tudor, Minal Patel, and others. Mapping and reprogramming human tissue microenvironments with mintflow. bioRxiv, 2025.

[BiMVelezSB+22]

Pau Badia-i-Mompel, Jesús Vélez Santiago, Jana Braunger, Celina Geiss, Daniel Dimitrov, Sophia Müller-Dott, Petr Taus, Aurelien Dugourd, Christian H Holland, Ricardo O Ramirez Flores, and others. Decoupler: ensemble of computational methods to infer biological activities from omics data. Bioinformatics advances, 2(1):vbac016, 2022.

[BAY21]

Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? arXiv preprint arXiv:2105.14491, 2021.

[CRH+25]

Helena L Crowell, Irene Ruano, Zedong Hu, Yourae Hong, Gin Caratù, Hubert Piessevaux, Ashley Heck, Rachel Liu, Max Walter, Megan Vandenberg, and others. Tracing colorectal malignancy transformation from cell to tissue scale. bioRxiv, 2025.

[DSK+25]

Mingze Dong, David G Su, Harriet Kluger, Rong Fan, and Yuval Kluger. Simvi disentangles intrinsic and spatial-induced cellular states in spatial omics data. Nature Communications, 16(1):2990, 2025.

[GLX+22]

Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, and others. A python library for probabilistic analysis of single-cell omics data. Nature biotechnology, 40(2):163–166, 2022.

[GPAM+14]

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 2014.

[HBK+22]

Leon Hetzel, Simon Boehm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, and Fabian Theis. Predicting cellular responses to novel drug perturbations at a single-cell resolution. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, 26711–26722. Curran Associates, Inc., 2022. URL: https://proceedings.neurips.cc/paper_files/paper/2022/file/aa933b5abc1be30baece1d230ec575a7-Paper-Conference.pdf.

[KTW+20]

Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. CoRR, 2020. URL: https://arxiv.org/abs/2004.11362, arXiv:2004.11362.

[KW13]

Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

[KFB+25]

Dominik Klein, Jonas Simon Fleck, Daniil Bobrovskiy, Lea Zimmermann, Sören Becker, Alessandro Palma, Leander Dony, Alejandro Tejada-Lapuerta, Guillaume Huguet, Hsiu-Chuan Lin, and others. Cellflow enables generative single-cell phenotype modeling with flow matching. bioRxiv, 2025.

[LKG+25]

Xiang Lin, Zhenglun Kong, Soumya Ghosh, Manolis Kellis, and Marinka Zitnik. Concert predicts niche-aware perturbation responses in spatial transcriptomics. bioRxiv, 2025.

[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.

[LKSDD+23]

Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, and others. Predicting cellular responses to complex perturbations in high-throughput screens. Molecular systems biology, 19(6):MSB202211517, 2023.

[LWT19]

Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis. Scgen predicts single-cell perturbation responses. Nature methods, 16(8):715–721, 2019.

[MCP+25]

Stathis Megas, Daniel G. Chen, Krzysztof Polanski, Moshe Eliasof, Carola-Bibiane Schönlieb, and Sarah A Teichmann. Estimation of single-cell and tissue perturbation effect in spatial transcriptomics via spatial causal disentanglement. In The Thirteenth International Conference on Learning Representations. 2025. URL: https://openreview.net/forum?id=Tqdsruwyac.

[SBBZ25]

Eric D Sun, Alejandro Buendia, Anne Brunet, and James Zou. Spatialprop: tissue perturbation modeling with spatially resolved single-cell transcriptomics. bioRxiv, 2025.

[WAT18]

F Alexander Wolf, Philipp Angerer, and Fabian J Theis. Scanpy: large-scale single-cell gene expression data analysis. Genome biology, 19(1):15, 2018.