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2023

Predicting cell morphological responses to perturbations using generative modeling

Palma, A., Theis, F. J.*, Lotfollahi,M*.

[bioRxiv (2023)]. [code].


An integrated cell atlas of the human lung in health and disease

Sikkema, L., Strobl, D., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ansari, M., Arguel, M., Apperloo, L., Bécavin, C., Berg, M., Chichelnitskiy, E., Chung, M., Collin, A., Gay, A. C. A., Hooshiar Kashani, B., Jain, M., Kapellos, T., Kole, T. M., …,Lotfollahi. M,...,Theis, F.

[Nature Medicine (2023)]. [code], [Mapping data to HLCA using scArhes],


Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M+., Klimovskaia Susmelj+, A., De Donno, C+., Hetzel, L., Ji, Y., Ibarra, I. L., ... & Theis, F. J.

[Molecular Systems Biology (2023)], [code], [Facebook AI blogpost], [state of AI report 2021], [featured cover].


The scverse project provides a computational ecosystem for single-cell omics data analysis

Virshup+ et al.

[Nature biotechnology (2023)], [community].


Best practices for single-cell analysis across modalities

Heumos+ et al.

[Nature Reviews Genetics (2023)], [code)].


Biologically Informed Deep Learning to Query Gene Programs in Single-Cell Atlases
Lotfollahi M, M+., Rybakov, S+., Hrovatin, K., Hediyeh-Zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J.

[Nature Cell Biology (2023)], [code].


Mapping cells to gene programs
Lotfollahi M, M+., Rybakov, S+., Hrovatin, K., Hediyeh-Zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J.

[Protocole exchange (2023)], [code].


Single-cell RNA sequencing uncovers heterogeneous transcriptional signatures in tumor-infiltrated dendritic cells in prostate cancer. Heliyon, 9(5), e15694.

Feriz, A. M., Khosrojerdi, A., Lotfollahi, M., Shamsaki, N., GhasemiGol, M., HosseiniGol, E., Fereidouni, M., Rohban, M. H., Sebzari, A. R., Saghafi, S., Leone, P., Silvestris, N., Safarpour, H., & Racanelli, V. (2023).

[Heliyon(2023)].

2022

Predicting single-cell perturbation responses for unseen drugs

Hetzel, L., Böhm, S., Kilbertus, N., Günnemann, S., Lotfollahi, M., & Theis, F. (2022).

[NeurIPS (2022)], [code].


Modelling method using a conditional variational autoencoder

Theis, F. J.,Lotfollahi, M., Wolf, F. A.

US Patent.


Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View

Nasab, R. Z., Ghamsari, M. R. E., Argha, A., Macphillamy, C., Beheshti, A., Alizadehsani, R., Lovell, N. H.,

Lotfollahi, M., & Alinejad-Rokny, H.

arXiv.


Single-cell reference mapping to construct and extend cell type hierarchies

Michielsen,L+.,Lotfollahi, M.+, Strobl, D., Sikkema, L., Reinders, M. J. T., Theis, F. J.,Mahfouz, A.

[BioRxv (2022)], [code].


Squidpy: a scalable framework for spatial omics analysis

Palla, G., Spitzer, H., Klein, M., Fischer, D., Schaar, A. C., Kuemmerle, L. B., Rybakov, S., Ibarra, I. L., Holmberg, O., Virshup, I., Lotfollahi, M., Richter, S. & Theis, F. J.

[Nature Methods (2022)], [code].


A Python library for probabilistic analysis of single-cell omics data

Gayoso, A.+, Lopez, R.+, Xing, G., Boyeau+, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C., Ashuach, T., Gabitto, M., Lotfollahi, M., … Yosef, N.

[Nature Biotechnology (2022)], [code].


Population-level integration of single-cell datasets enables multi-scale analysis across samples.

De Donno, C., Hediyeh-Zadeh, S., Wagenstetter, M., Moinfar, A. A., Zappia, L.,

Lotfollahi, M.* , & Theis, F. J*.

[code], [bioRxv (2022)].


An integrated cell atlas of the human lung in health and disease

Sikkema, L., Strobl, D., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ansari, M., Arguel, M., Apperloo, L., Bécavin, C., Berg, M., Chichelnitskiy, E., Chung, M., Collin, A., Gay, A. C. A., Hooshiar Kashani, B., Jain, M., Kapellos, T., Kole, T. M., …,Lotfollahi. M,...,Theis, F.

[code],

[bioRxv (2022)].


Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

Gayoso, A., Weiler, P., Lotfollahi, M., Klein, D., Hong, J., Streets, A., Theis, F. J., & Yosef, N.

[code],

[bioRxv (2022)].


MultiCPA: Multimodal Compositional Perturbation Autoencoder

Inecik, K., Uhlmann, A.,

Lotfollahi,M.* , Theis,F*.

[ICML Workshop on Computational Biology (WCB) 2022], [bioRxv], [code].


Continual single-cell architecture surgery for reference mapping.

Hediyeh-zadeh, S.,

Lotfollahi, M.* , & Theis, F*.

[ICML Workshop on Computational Biology (WCB) 2022],


Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M. & Others.

[Nature Biotechnology (2022)], [code], [MDSI best paper award], [featured cover in Nature Biotechnology].

2021

Multigrate: Single-Cell Multi-Omic Data Integration
Lotfollahi. M+., Litinetskaya, A+. and Theis, F. J.

[contributed talk Award at ICML Workshop on Computational Biology 2021], [code], [bioRxv (2022)].


Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis

Drost, F., An, Y., Dratva, L. M., Lindeboom, R. G. H., Haniffa, M., Teichmann, S. A., Theis, F., Lotfollahi, M.*, Schubert, B*.

[ICML Workshop on Computational Biology 2021], [code], [bioRxv (2021)].


Machine learning for perturbational single-cell omics

Ji, Y., Lotfollahi, M., Wolf, F. A. & Theis, F. J.

[Cell Systems(2021)], [data resource].

2020

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data
Lotfollahi, M.+, Dony, L.+, Agarwala, H.+, & Theis, F. J.

[ICML Workshop on Computational Biology (WCB) 2020], [spotlight talk ICML WCB 2020], [code]


Conditional out-of-distribution generation for unpaired data using transfer VAE

Lotfollahi, M., Naghipourfar, M., Theis, F. J. & Wolf, F. A.

[Bioinformatics(2020)], [code], [talk at ECCB 2020 (21.18% accept rate)].


Learning Interpretable Latent Autoencoder Representations with Annotations of Feature Sets

S. Rybakov, M. Lotfollahi, F.J. Theis, F.A. Wolf.

[Machine Learning in Computational Biology (2020)], [code].

2019

scGen predicts single-cell perturbation responses.

Lotfollahi, M., Wolf, F. A. & Theis, F. J.

[Nature Methods(2019)], [code], [press].


Deep packet: a novel approach for encrypted traffic classification using deep learning
Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M.

[soft computing(2019)].