Gayoso, Adam

Cell2location maps fine-grained cell types in spatial transcriptomics

Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 1,279

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science

Single-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

Cited by 1,056

Palantir characterizes cell fate continuities in human hematopoiesis

Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D. (2018) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 836

Characterization of cell fate probabilities in single-cell data with Palantir

Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D. (2019) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 833

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

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

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 592

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 373

Cell types of origin of the cell-free transcriptome

Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Overall Project Direction and Coordination, Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 116

Enhancing scientific discoveries in molecular biology with deep generative models

Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology

Single-cell and spatial transcriptomicsMachine Learning in Bioinformatics

Cited by 82

Deep generative modeling of sample-level heterogeneity in single-cell genomics

Boyeau, P., Hong, J., Gayoso, A., Kim, M., McFaline-Figueroa, J. L., Jordan, M. I., Azizi, E., Ergen, C., & Yosef, N. (2025) · Nature Methods

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 55