Research Group Fabian Theis
Fabian Theis
Prof. Dr.
Principal Investigator
Fabian Theis
holds the Chair of Mathematical Modeling of Biological Systems and TU Munich and is director of the Institute of Computational Biology at the Helmholtz Zentrum München.
He conducts research in the field of computational biology. The main focus of his work is the application of machine learning methods to biological questions, in particular as a means of modeling cell heterogeneities on the basis of single cell analyses and also of integrating ‘omics’ data into systems medicine approaches.
Team members @MCML
PhD Students
Jan Engelmann
→ Group Fabian Theis
Mathematical Modelling of Biological Systems
Leon Hetzel
→ Group Fabian Theis
Mathematical Modelling of Biological Systems
Till Richter
→ Group Fabian Theis
Mathematical Modelling of Biological Systems
Recent News @MCML
Publications @MCML
2025
[50]
M. Bahrami • A. Tejada-Lapuerta • S. Becker • F. S. Hashemi G. • F. J. Theis
scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstruction.
Preprint (Oct. 2025). DOI
scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstruction.
Preprint (Oct. 2025). DOI
[49]
T. Uscidda • L. Eyring • K. Roth • F. J. Theis • Z. Akata • M. Cuturi
Disentangled Representation Learning with the Gromov-Monge Gap.
ICLR 2025 - 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. URL
Disentangled Representation Learning with the Gromov-Monge Gap.
ICLR 2025 - 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. URL
[48]
A. Tejada-Lapuerta • P. Bertin • S. Bauer • H. Aliee • Y. Bengio • F. J. Theis
Causal machine learning for single-cell genomics.
Nature Genetics. Mar. 2025. DOI
Causal machine learning for single-cell genomics.
Nature Genetics. Mar. 2025. DOI
[47]
M. E. Consens • C. Dufault • M. Wainberg • D. Forster • M. Karimzadeh • H. Goodarzi • F. J. Theis • A. Moses • B. Wang
Transformers and genome language models.
Nature Machine Intelligence. Mar. 2025. DOI
Transformers and genome language models.
Nature Machine Intelligence. Mar. 2025. DOI
[46]
P. Bertin • J. D. Viviano • A. Tejada-Lapuerta • W. Wang • S. Bauer • F. J. Theis • Y. Bengio
A scalable gene network model of regulatory dynamics in single cells.
Preprint (Mar. 2025). arXiv
A scalable gene network model of regulatory dynamics in single cells.
Preprint (Mar. 2025). arXiv
2024
[45]
L. B. Kuemmerle • M. D. Luecken • A. B. Firsova • L. Barros de Andrade e Sousa • L. Straßer • I. I. Mekki • F. Campi • L. Heumos • M. Shulman • V. Beliaeva • S. Hediyeh-Zadeh • A. C. Schaar • K. T. Mahbubani • A. Sountoulidis • T. Balassa • F. Kovacs • P. Horvath • M. Piraud • A. Ertürk • C. Samakovlis • F. J. Theis
Probe set selection for targeted spatial transcriptomics.
Nature Methods 21. Dec. 2024. DOI
Probe set selection for targeted spatial transcriptomics.
Nature Methods 21. Dec. 2024. DOI
[44]
A. Szałata • K. Hrovatin • S. Becker • A. Tejada-Lapuerta • H. Cui • B. Wang • F. J. Theis
Transformers in single-cell omics: a review and new perspectives.
Nature Methods 21. Aug. 2024. DOI
Transformers in single-cell omics: a review and new perspectives.
Nature Methods 21. Aug. 2024. DOI
[43]
T. Uscidda • L. Eyring • K. Roth • F. J. Theis • Z. Akata • M. Cuturi
Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv
Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv
[42]
M. Ali • M. Kuijs • S. Hediyeh-zadeh • T. Treis • K. Hrovatin • G. Palla • A. C. Schaar • F. J. Theis
GraphCompass: spatial metrics for differential analyses of cell organization across conditions.
Bioinformatics 40.Supplement 1. Jul. 2024. DOI
GraphCompass: spatial metrics for differential analyses of cell organization across conditions.
Bioinformatics 40.Supplement 1. Jul. 2024. DOI
[41]
T. Wollschläger • N. Kemper • L. Hetzel • J. Sommer • S. Günnemann
Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
Preprint (Jun. 2024). arXiv
Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
Preprint (Jun. 2024). arXiv
[40]
L. Eyring • D. Klein • T. Uscidda • G. Palla • N. Kilbertus • Z. Akata • F. J. Theis
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation.
ICLR 2024 - 12th International Conference on Learning Representations. Vienna, Austria, May 07-11, 2024. URL
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation.
ICLR 2024 - 12th International Conference on Learning Representations. Vienna, Austria, May 07-11, 2024. URL
[39]
A. Gayoso • P. Weiler • M. Lotfollahi • D. Klein • J. Hong • A. Streets • F. J. Theis • N. Yosef
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.
Nature Methods 21. Jan. 2024. DOI
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.
Nature Methods 21. Jan. 2024. DOI
2023
[38]
M. Lotfollahi • A. K. Susmelj • C. De Donno • L. Hetzel • Y. Ji • I. L. Ibarra • S. R. Srivatsan • M. Naghipourfar • R. M. Daza • B. Martin • J. Shendure • J. L. McFaline‐Figueroa • P. Boyeau • F. A. Wolf • N. Yakubova • S. Günnemann • C. Trapnell • D. Lopez‐Paz • F. J. Theis
Predicting cellular responses to complex perturbations in high‐throughput screens.
Molecular Systems Biology 19.e11517. Jun. 2023. DOI
Predicting cellular responses to complex perturbations in high‐throughput screens.
Molecular Systems Biology 19.e11517. Jun. 2023. DOI
[37]
J. Sommer • L. Hetzel • D. Lüdke • F. J. Theis • S. Günnemann
The power of motifs as inductive bias for learning molecular distributions.
Preprint (Jun. 2023). arXiv
The power of motifs as inductive bias for learning molecular distributions.
Preprint (Jun. 2023). arXiv
[36]
D. S. Fischer • A. C. Schaar • F. J. Theis
Modeling intercellular communication in tissues using spatial graphs of cell.
Nature Biotechnology 41. Mar. 2023. DOI
Modeling intercellular communication in tissues using spatial graphs of cell.
Nature Biotechnology 41. Mar. 2023. DOI
[35]
L. Heumos • A. C. Schaar • C. Lance • A. Litinetskaya • F. Drost • L. Zappia • M. D. Lücken • D. C. Strobl • J. Henao • F. Curion • S.-c. Best Practices Consortium • H. B. Schiller • F. J. Theis
Best practices for single-cell analysis across modalities.
Nature Reviews Genetics 24. Mar. 2023. DOI
Best practices for single-cell analysis across modalities.
Nature Reviews Genetics 24. Mar. 2023. DOI
2022
[34]
H. Aliee • T. Richter • M. Solonin • I. Ibarra • F. J. Theis • N. Kilbertus
Sparsity in Continuous-Depth Neural Networks.
NeurIPS 2022 - 36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022. URL
Sparsity in Continuous-Depth Neural Networks.
NeurIPS 2022 - 36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022. URL
[33]
L. Hetzel • S. Boehm • N. Kilbertus • S. Günnemann • M. Lotfollahi • F. J. Theis
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
NeurIPS 2022 - 36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022. URL
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
NeurIPS 2022 - 36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022. URL
[32]
D. S. Fischer • M. Ali • S. Richter • A. Ertürk • F. J. Theis
Graph neural networks learn emergent tissue properties from spatial molecular profiles.
Preprint (Nov. 2022). DOI
Graph neural networks learn emergent tissue properties from spatial molecular profiles.
Preprint (Nov. 2022). DOI
[31]
B. A. Hersbach • D. S. Fischer • G. Masserdotti • Deeksha • K. Mojžišová • T. Waltzhöni • D. Rodriguez‐Terrones • M. Heinig • F. J. Theis • M. Götz • S. H. Stricker
Probing cell identity hierarchies by fate titration and collision during direct reprogramming.
Molecular Systems Biology 18.e11129. Sep. 2022. DOI
Probing cell identity hierarchies by fate titration and collision during direct reprogramming.
Molecular Systems Biology 18.e11129. Sep. 2022. DOI
[30]
E. M. A. Slob • A. Faiz • J. van Nijnatten • S. J. H. Vijverberg • C. Longo • M. Kutlu • F. T. Chew • Y. Y. Sio • E. Herrera-Luis • A. Espuela-Ortiz • J. Perez-Garcia • M. Pino-Yanes • E. G. Burchard • U. Potočnik • M. Gorenjak • C. Palmer • C. Maroteau • S. Turner • K. Verhamme • L. Karimi • S. Mukhopadhyay • W. Timens • P. S. Hiemstra • M. W. Pijnenburg • M. Neighbors • M. A. Grimbaldeston • G. W. Tew • C. A. Brandsma • V. Berce • H. Aliee • F. J. Theis • D. D. Sin • X. Li • M. van den Berge • A. H. Zee • G. H. Koppelman
Association of bronchial steroid inducible methylation quantitative trait loci with asthma and chronic obstructive pulmonary disease treatment response.
Clinical and Translational Allergy 12.8. Aug. 2022. DOI
Association of bronchial steroid inducible methylation quantitative trait loci with asthma and chronic obstructive pulmonary disease treatment response.
Clinical and Translational Allergy 12.8. Aug. 2022. DOI
[29]
M. Lotfollahi • M. Naghipourfar • M. D. Luecken • M. Khajavi • M. Büttner • M. Wagenstetter • Z. Avsec • A. Gayoso • N. Yosef • M. Interlandi • S. Rybakov • A. V. Misharin • F. J. Theis
Mapping single-cell data to reference atlases by transfer learning.
Nature Biotechnology 40. Aug. 2022. DOI
Mapping single-cell data to reference atlases by transfer learning.
Nature Biotechnology 40. Aug. 2022. DOI
[28]
L. Hetzel • S. Boehm • N. Kilbertus • S. Günnemann • M. Lotfollahi • F. J. Theis
Predicting single-cell perturbation responses for unseen drugs.
MLDD @ICML 2022 - Workshop on Machine Learning for Drug Discovery at the 39th International Conference on Machine Learning. Baltimore, MD, USA, Jul 17-23, 2022. URL
Predicting single-cell perturbation responses for unseen drugs.
MLDD @ICML 2022 - Workshop on Machine Learning for Drug Discovery at the 39th International Conference on Machine Learning. Baltimore, MD, USA, Jul 17-23, 2022. URL
[27]
K. Baßler • W. Fujii • T. S. Kapellos • E. Dudkin • N. Reusch • A. Horne • B. Reiz • M. D. Luecken • C. Osei-Sarpong • S. Warnat-Herresthal • L. Bonaguro • J. Schulte-Schrepping • A. Wagner • P. Günther • C. Pizarro • T. Schreiber • R. Knoll • L. Holsten • C. Kröger • E. De Domenico • M. Becker • K. Händler • C. T. Wohnhaas • F. Baumgartner • M. Köhler • H. Theis • M. Kraut • M. H. Wadsworth • T. K. Hughes • H. J. Ferreira • E. Hinkley • I. H. Kaltheuner • M. Geyer • C. Thiele • A. K. Shalek • A. Feißt • D. Thomas • H. Dickten • M. Beyer • P. Baum • N. Yosef • A. C. Aschenbrenner • T. Ulas • J. Hasenauer • F. J. Theis • D. Skowasch • J. L. Schultze
Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.
Frontiers in Immunology 13. Jul. 2022. DOI
Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.
Frontiers in Immunology 13. Jul. 2022. DOI
[26]
G. Palla • H. Spitzer • M. Klein • D. S. Fischer • A. C. Schaar • L. B. Kuemmerle • S. Rybakov • I. L. Ibarra • O. Holmberg • I. Virshup • M. Lotfollahi • S. Richter • F. J. Theis
Squidpy: a scalable framework for spatial omics analysis.
Nature Methods 19. Jan. 2022. DOI
Squidpy: a scalable framework for spatial omics analysis.
Nature Methods 19. Jan. 2022. DOI
[25]
M. Lange • V. Bergen • M. Klein • M. Setty • B. Reuter • M. Bakhti • H. Lickert • M. Ansari • J. Schniering • H. B. Schiller • D. Pe’er • F. J. Theis
CellRank for directed single-cell fate mapping.
Nature Methods 19.2. Jan. 2022. DOI
CellRank for directed single-cell fate mapping.
Nature Methods 19.2. Jan. 2022. DOI
2021
[24]
L. Hetzel • D. S. Fischer • S. Günnemann • F. J. Theis
Graph representation learning for single-cell biology.
Current Opinion in Systems Biology 28.100347. Dec. 2021. DOI
Graph representation learning for single-cell biology.
Current Opinion in Systems Biology 28.100347. Dec. 2021. DOI
[23]
K. T. Schmid • B. Höllbacher • C. Cruceanu • A. Böttcher • H. Lickert • E. B. Binder • F. J. Theis • M. Heinig
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.
Nature Communications 12.6625. Nov. 2021. DOI
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.
Nature Communications 12.6625. Nov. 2021. DOI
[22]
C. M. Verdun • T. Fuchs • P. Harar • D. Elbrächter • D. S. Fischer • J. Berner • P. Grohs • F. J. Theis • F. Krahmer
Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies.
Frontiers in Public Health 9. Aug. 2021. DOI
Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies.
Frontiers in Public Health 9. Aug. 2021. DOI
[21]
D. S. Fischer • L. Dony • M. König • A. Moeed • L. Zappia • L. Heumos • S. Tritschler • O. Holmberg • H. Aliee • F. J. Theis
Sfaira accelerates data and model reuse in single cell genomics.
Genome Biology 22.248. Aug. 2021. DOI
Sfaira accelerates data and model reuse in single cell genomics.
Genome Biology 22.248. Aug. 2021. DOI
[20]
V. Bergen • R. A. Soldatov • P. V. Kharchenko • F. J. Theis
RNA velocity—current challenges and future perspectives.
Molecular Systems Biology 17.e10282. Aug. 2021. DOI
RNA velocity—current challenges and future perspectives.
Molecular Systems Biology 17.e10282. Aug. 2021. DOI
[19]
D. S. Fischer • M. Ansari • K. I. Wagner • S. Jarosch • Y. • C. H. Mayr • M. Lang • E. D’Ippolito • M. Hammel • L. Mateyka • S. Weber • L. S. Wolff • K. Witter • I. E. Fernandez • G. Leuschner • K. Milger • M. Frankenberger • L. Nowak • K. Heinig-Menhard • I. Koch • M. G. Stoleriu • A. Hilgendorff • J. Behr • A. Pichlmair • B. Schubert • F. J. Theis • D. H. Busch • H. B. Schiller • K. Schober
Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’.
Nature Communications 12.1. Jul. 2021. DOI
Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’.
Nature Communications 12.1. Jul. 2021. DOI
[18]
Y. Ji • M. Lotfollahi • F. A. Wolf • F. J. Theis
Machine learning for perturbational single-cell omics.
Cell Systems 12.6. Jun. 2021. DOI GitHub
Machine learning for perturbational single-cell omics.
Cell Systems 12.6. Jun. 2021. DOI GitHub
[17]
D. S. Fischer • A. C. Schaar • F. J. Theis
Learning cell communication from spatial graphs of cells.
Preprint (Jun. 2021). DOI
Learning cell communication from spatial graphs of cells.
Preprint (Jun. 2021). DOI
[16]
M. Lotfollahi • A. K. Susmelj • C. De Donno • Y. Ji • I. L. Ibarra • F. A. Wolf • N. Yakubova • F. J. Theis • D. Lopez-Paz
Compositional perturbation autoencoder for single-cell response modeling.
Preprint (May. 2021). DOI
Compositional perturbation autoencoder for single-cell response modeling.
Preprint (May. 2021). DOI
[15]
G. Palla • H. Spitzer • M. Klein • D. S. Fischer • A. C. Schaar • L. B. Kuemmerle • S. Rybakov • I. L. Ibarra • O. Holmberg • I. Virshup • M. Lotfollahi • S. Richter • F. J. Theis
Squidpy: a scalable framework for spatial single cell analysis.
Preprint (Apr. 2021). DOI
Squidpy: a scalable framework for spatial single cell analysis.
Preprint (Apr. 2021). DOI
[14]
F. Meier • N. D. Köhler • A.-D. Brunner • J.-M. H. Wanka • E. Voytik • M. T. Strauss • F. J. Theis • M. Mann
Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Nature Communications 12.1185. Feb. 2021. DOI
Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Nature Communications 12.1185. Feb. 2021. DOI
[13]
J. P. Lopez • E. Brivio • A. Santambrogio • C. De Donno • A. Kos • M. Peters • N. Rost • D. Czamara • T. M. Brückl • S. Roeh • M. L. Pöhlmann • C. Engelhardt • A. Ressle • R. Stoffel • A. Tontsch • J. M. Villamizar • M. Reincke • A. Riester • S. Sbiera • M. Fassnacht • H. S. Mayberg • W. E. Craighead • B. W. Dunlop • C. B. Nemeroff • M. V. Schmidt • E. B. Binder • F. J. Theis • F. Beuschlein • C. L. Andoniadou • A. Chen
Single-cell molecular profiling of all three components of the HPA axis reveals adrenal ABCB1 as a regulator of stress adaptation.
Science Advances 7.5. Jan. 2021. DOI
Single-cell molecular profiling of all three components of the HPA axis reveals adrenal ABCB1 as a regulator of stress adaptation.
Science Advances 7.5. Jan. 2021. DOI
2020
[12]
M. Lotfollahi • M. Naghipourfar • F. J. Theis • F. A. Wolf
Conditional out-of-distribution generation for unpaired data using transfer VAE.
Bioinformatics 36.Supplement 2. Dec. 2020. DOI
Conditional out-of-distribution generation for unpaired data using transfer VAE.
Bioinformatics 36.Supplement 2. Dec. 2020. DOI
[11]
N.-K. Chlis • A. Karlas • N.-A. Fasoula • M. Kallmayer • H.-H. Eckstein • F. J. Theis • V. Ntziachristos • C. Marr
A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography.
Photoacoustics 20.100203. Dec. 2020. DOI
A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography.
Photoacoustics 20.100203. Dec. 2020. DOI
[10]
N.-K. Chlis • L. Rausch • T. Brocker • J. Kranich • F. J. Theis
Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
Nucleic Acids Research 48.20. Nov. 2020. DOI
Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
Nucleic Acids Research 48.20. Nov. 2020. DOI
[9]
D. S. Fischer • Y. Wu • B. Schubert • F. J. Theis
Predicting antigen specificity of single T cells based on TCR CDR3 regions.
Molecular Systems Biology 16.8. Aug. 2020. DOI
Predicting antigen specificity of single T cells based on TCR CDR3 regions.
Molecular Systems Biology 16.8. Aug. 2020. DOI
[8]
V. Bergen • M. Lange • S. Peidli • F. A. Wolf • F. J. Theis
Generalizing RNA velocity to transient cell states through dynamical modeling.
Nature Biotechnology 38. Aug. 2020. DOI
Generalizing RNA velocity to transient cell states through dynamical modeling.
Nature Biotechnology 38. Aug. 2020. DOI
[7]
J. Kranich • N.-K. Chlis • L. Rausch • A. Latha • M. Schifferer • T. Kurz • A. F.-A. Kia • M. Simons • F. J. Theis • T. Brocker
In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.
Journal of Extracellular Vesicles 9.1. Jul. 2020. DOI
In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.
Journal of Extracellular Vesicles 9.1. Jul. 2020. DOI
[6]
M. Lotfollahi • M. Naghipourfar • M. D. Luecken • M. Khajavi • M. Büttner • Z. Avsec • A. V. Misharin • F. J. Theis
Query to reference single-cell integration with transfer learning.
Preprint (Jul. 2020). DOI
Query to reference single-cell integration with transfer learning.
Preprint (Jul. 2020). DOI
[5]
K. Baßler • W. Fujii • T. S. Kapellos • A. Horne • B. Reiz • E. Dudkin • M. Lücken • N. Reusch • C. Osei-Sarpong • S. Warnat-Herresthal • A. Wagner • L. Bonaguro • P. Günther • C. Pizarro • T. Schreiber • M. Becker • K. Händler • C. T. Wohnhaas • F. Baumgartner • M. Köhler • H. Theis • M. Kraut • M. H. Wadsworth • T. K. Hughes • H. J. G. Ferreira • J. Schulte-Schrepping • E. Hinkley • I. H. Kaltheuner • M. Geyer • C. Thiele • A. K. Shalek • A. Feißt • D. Thomas • H. Dickten • M. Beyer • P. Baum • N. Yosef • A. C. Aschenbrenner • T. Ulas • J. Hasenauer • F. J. Theis • D. Skowasch • J. L. Schultze
Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease.
Preprint (May. 2020). DOI
Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease.
Preprint (May. 2020). DOI
[4]
S. Sachs • A. Bastidas-Ponce • S. Tritschler • M. Bakhti • A. Böttcher • M. A. Sánchez-Garrido • M. Tarquis-Medina • M. Kleinert • K. Fischer • S. Jall • A. Harger • E. Bader • S. Roscioni • S. Ussar • A. Feuchtinger • B. Yesildag • A. Neelakandhan • C. B. Jensen • M. Cornu • B. Yang • B. Finan • R. D. DiMarchi • M. H. Tschöp • F. J. Theis • S. M. Hofmann • T. D. Müller • H. Lickert
Targeted pharmacological therapy restores β-cell function for diabetes remission.
Nature Metabolism 2. Feb. 2020. DOI
Targeted pharmacological therapy restores β-cell function for diabetes remission.
Nature Metabolism 2. Feb. 2020. DOI
2019
[3]
M. Lotfollahi • F. A. Wolf • F. J. Theis
scGen predicts single-cell perturbation responses.
Nature Methods 16.8. Jul. 2019. DOI GitHub
scGen predicts single-cell perturbation responses.
Nature Methods 16.8. Jul. 2019. DOI GitHub
[2]
F. Erhard • M. A. P. Baptista • T. Krammer • T. Hennig • M. Lange • P. Arampatzi • C. S. Jürges • F. J. Theis • A.-E. Saliba • L. Dölken
scSLAM-seq reveals core features of transcription dynamics in single cells.
Nature 571. Jul. 2019. DOI
scSLAM-seq reveals core features of transcription dynamics in single cells.
Nature 571. Jul. 2019. DOI
[1]
M. D. Luecken • F. J. Theis
Current best practices in single‐cell RNA‐seq analysis: a tutorial.
Molecular Systems Biology 15.e8746. Jun. 2019. DOI GitHub
Current best practices in single‐cell RNA‐seq analysis: a tutorial.
Molecular Systems Biology 15.e8746. Jun. 2019. DOI GitHub
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