| Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data F Drost, Y An, I Bonafonte-Pardàs, LM Dratva, RGH Lindeboom, M Haniffa, ... Nature Communications 15 (1), 5577, 2024 | 16* | 2024 |
| The future of rapid and automated single-cell data analysis using reference mapping M Lotfollahi, Y Hao, FJ Theis, R Satija Cell 187 (10), 2343-2358, 2024 | 3 | 2024 |
| Deep learning in spatially resolved transcriptfomics: a comprehensive technical view R Zahedi, R Ghamsari, A Argha, C Macphillamy, A Beheshti, ... Briefings in Bioinformatics 25 (2), bbae082, 2024 | 15* | 2024 |
| Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning S Birk, I Bonafonte-Pardàs, AM Feriz, A Boxall, E Agirre, F Memi, ... bioRxiv, 2024.02. 21.581428, 2024 | 2 | 2024 |
| Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells A Gayoso, P Weiler, M Lotfollahi, D Klein, J Hong, A Streets, FJ Theis, ... Nature methods 21 (1), 50-59, 2024 | 29 | 2024 |
| An integrated single-cell reference atlas of the human endometrium M Marečková, L Garcia-Alonso, M Moullet, V Lorenzi, R Petryszak, ... bioRxiv, 2023.11. 03.564728, 2023 | 2 | 2023 |
| Population-level integration of single-cell datasets enables multi-scale analysis across samples C De Donno, S Hediyeh-Zadeh, AA Moinfar, M Wagenstetter, L Zappia, ... Nature Methods, 1-10, 2023 | 25 | 2023 |
| Single-cell reference mapping to construct and extend cell-type hierarchies L Michielsen, M Lotfollahi, D Strobl, L Sikkema, MJT Reinders, FJ Theis, ... NAR Genomics and Bioinformatics 5 (3), lqad070, 2023 | 11 | 2023 |
| Best practices for single-cell analysis across modalities L Heumos, AC Schaar, C Lance, A Litinetskaya, F Drost, L Zappia, ... Nature Reviews Genetics 24 (8), 550-572, 2023 | 287 | 2023 |
| An integrated cell atlas of the lung in health and disease L Sikkema, C Ramírez-Suástegui, DC Strobl, TE Gillett, L Zappia, ... Nature medicine 29 (6), 1563-1577, 2023 | 241* | 2023 |
| Predicting cellular responses to complex perturbations in high‐throughput screens M Lotfollahi, A Klimovskaia Susmelj, C De Donno, L Hetzel, Y Ji, IL Ibarra, ... Molecular Systems Biology, e11517, 2023 | 122* | 2023 |
| The scverse project provides a computational ecosystem for single-cell omics data analysis I Virshup, D Bredikhin, L Heumos, G Palla, G Sturm, A Gayoso, I Kats, ... Nature biotechnology 41 (5), 604-606, 2023 | 73 | 2023 |
| Mapping cells to gene programs M Lotfollahi, S Rybakov, K Hrovatin, S Hediyeh-zadeh, C Talavera-Lopez, ... | 1 | 2023 |
| Biologically informed deep learning to query gene programs in single-cell atlases M Lotfollahi, S Rybakov, K Hrovatin, S Hediyeh-Zadeh, C Talavera-López, ... Nature Cell Biology 25 (2), 337-350, 2023 | 62* | 2023 |
| Predicting cell morphological responses to perturbations using generative modeling A Palma, FJ Theis, M Lotfollahi bioRxiv, 2023.07. 17.549216, 2023 | 2 | 2023 |
| Predicting cellular responses to novel drug perturbations at a single-cell resolution L Hetzel, S Boehm, N Kilbertus, S Günnemann, M Lotfollahi, F Theis Advances in Neural Information Processing Systems 35, 26711-26722, 2022 | 34* | 2022 |
| Modelling method using a conditional variational autoencoder F Theis, M Lotfollahi, FA Wolf US Patent App. 17/763,501, 2022 | | 2022 |
| Squidpy: a scalable framework for spatial omics analysis G Palla, H Spitzer, M Klein, D Fischer, AC Schaar, LB Kuemmerle, ... Nature methods 19 (2), 171-178, 2022 | 429 | 2022 |
| A Python library for probabilistic analysis of single-cell omics data A Gayoso, R Lopez, G Xing, P Boyeau, V Valiollah Pour Amiri, J Hong, ... Nature biotechnology 40 (2), 163-166, 2022 | 308 | 2022 |
| Modeling single-cell perturbations using deep learning M Lotfollahi Technische Universität München, 2022 | | 2022 |
Fabian J TheisHelmholtz Munich, Technical University of Munich在 helmholtz-muenchen.de 的电子邮件经过验证
