Lotfollahi, Mohammad

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics

Single-cell and spatial transcriptomicsBioinformatics and Genomic Networks

Cited by 1,051

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., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 789

scGen predicts single-cell perturbation responses

Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 661

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., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 602

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., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 307

Machine learning for perturbational single-cell omics

Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 137

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

Lotfollahi, M., Naghipourfar, M., Theis, F. J., & Wolf, F. A. (2020) · Bioinformatics

Single-cell and spatial transcriptomicsGenerative Adversarial Networks and Image Synthesis

Cited by 130

Biologically informed deep learning to query gene programs in single-cell atlases

Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 100

Biologically informed deep learning to infer gene program activity in single cells

Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2022) · Nature Cell Biology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 100

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory)

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 21

MultiCPA: Multimodal Compositional Perturbation Autoencoder

Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 16