Nir Yosef

33.9k total citations · 13 hit papers
116 papers, 13.4k citations indexed

About

Nir Yosef is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Nir Yosef has authored 116 papers receiving a total of 13.4k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Molecular Biology, 42 papers in Immunology and 18 papers in Cancer Research. Recurrent topics in Nir Yosef's work include Single-cell and spatial transcriptomics (43 papers), Immune Cell Function and Interaction (26 papers) and T-cell and B-cell Immunology (22 papers). Nir Yosef is often cited by papers focused on Single-cell and spatial transcriptomics (43 papers), Immune Cell Function and Interaction (26 papers) and T-cell and B-cell Immunology (22 papers). Nir Yosef collaborates with scholars based in United States, Israel and Germany. Nir Yosef's co-authors include Aviv Regev, Romain Lopez, Jeffrey Regier, Michael I. Jordan, Michael B. Cole, Vijay K. Kuchroo, Chuan Wu, Elizabeth Purdom, Sandrine Dudoit and John Ngai and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Nir Yosef

110 papers receiving 13.3k citations

Hit Papers

Slingshot: cell lineage and pseudotime inference ... 2012 2026 2016 2021 2018 2018 2013 2012 2013 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
    2018 1.5k citations Nir Yosef et al. profile →
  2. Deep generative modeling for single-cell transcriptomics
    2018 1.1k citations Romain Lopez, Jeffrey Regier et al. Nature Methods profile →
  3. Sodium chloride drives autoimmune disease by the induction of pathogenic TH17 cells
    2013 1.0k citations Nir Yosef et al. Nature profile →
  4. Induction and molecular signature of pathogenic TH17 cells
    2012 914 citations Nir Yosef, Sheng Xiao et al. profile →
  5. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
    2013 853 citations Alex K. Shalek, Rona S. Gertner et al. Nature profile →
  6. Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1
    2013 813 citations Chuan Wu, Nir Yosef et al. Nature profile →
  7. The epigenetic landscape of T cell exhaustion
    2016 659 citations Nir Yosef et al. Science profile →
  8. Revealing the vectors of cellular identity with single-cell genomics
    2016 401 citations Allon Wagner, Aviv Regev et al. Nature Biotechnology profile →
  9. Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
    2020 256 citations Nir Yosef et al. Nature Communications profile →
  10. Joint probabilistic modeling of single-cell multi-omic data with totalVI
    2021 249 citations Adam Gayoso, Zoë Steier et al. Nature Methods profile →
  11. Mapping single-cell data to reference atlases by transfer learning
    2021 247 citations Adam Gayoso, Nir Yosef et al. Nature Biotechnology profile →
  12. Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
    2021 236 citations Romain Lopez, Jeffrey Regier et al. profile →
  13. MultiVI: deep generative model for the integration of multimodal data
    2023 118 citations Tal Ashuach, Mariano I. Gabitto et al. Nature Methods profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nir Yosef United States 46 7.8k 4.5k 1.6k 1.6k 1.1k 116 13.4k
Michael R. Lamprecht United States 10 10.3k 1.3× 1.4k 0.3× 5.6k 3.4× 1.7k 1.0× 1.8k 1.7× 14 17.9k
Norma Neff United States 48 9.1k 1.2× 1.6k 0.4× 1.9k 1.2× 1.3k 0.8× 330 0.3× 92 13.4k
Berthold Göttgens United Kingdom 67 13.2k 1.7× 4.1k 0.9× 1.7k 1.0× 1.5k 0.9× 559 0.5× 296 18.5k
Evan W. Newell United States 52 4.5k 0.6× 5.5k 1.2× 708 0.4× 2.2k 1.4× 697 0.7× 149 11.2k
David A. Guertin United States 39 14.8k 1.9× 2.2k 0.5× 2.2k 1.3× 2.1k 1.3× 1.4k 1.3× 63 21.2k
Philippe P. Roux Canada 50 10.5k 1.3× 1.9k 0.4× 1.4k 0.9× 1.9k 1.2× 154 0.1× 105 15.4k
Li Li China 57 8.9k 1.1× 1.6k 0.4× 2.8k 1.7× 1.9k 1.2× 160 0.2× 493 13.6k
Avi Ma’ayan United States 63 16.8k 2.2× 5.2k 1.2× 3.6k 2.2× 3.0k 1.9× 445 0.4× 186 29.4k
Qiaonan Duan United States 12 7.5k 1.0× 2.2k 0.5× 2.0k 1.2× 1.3k 0.8× 157 0.1× 20 12.7k
Zichen Wang United States 32 9.2k 1.2× 2.2k 0.5× 2.4k 1.5× 1.6k 1.0× 161 0.2× 90 15.4k

Countries citing papers authored by Nir Yosef

Since Specialization
Citations

This map shows the geographic impact of Nir Yosef's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nir Yosef with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Yosef more than expected).

Fields of papers citing papers by Nir Yosef

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nir Yosef. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nir Yosef. The network helps show where Nir Yosef may publish in the future.

Co-authorship network of co-authors of Nir Yosef

This figure shows the co-authorship network connecting the top 25 collaborators of Nir Yosef. A scholar is included among the top collaborators of Nir Yosef based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nir Yosef. Nir Yosef is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Koblan, Luke W., Kathryn E. Yost, Pu Zheng, et al.. (2025). High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer. Science. 390(6770). eadx3800–eadx3800. 3 indexed citations
3.
Boyeau, Pierre, Justin Hong, Adam Gayoso, et al.. (2025). Deep generative modeling of sample-level heterogeneity in single-cell genomics. Nature Methods. 22(11). 2264–2274. 2 indexed citations
4.
Liu, Jiayi, Tal Ashuach, Fumitaka Inoue, et al.. (2024). Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework. Nucleic Acids Research. 52(4). 1613–1627. 1 indexed citations
5.
Sinha, Sanju, Rahulsimham Vegesna, Sumit Mukherjee, et al.. (2024). PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nature Cancer. 5(6). 938–952. 48 indexed citations
6.
Ashuach, Tal, et al.. (2023). MultiVI: deep generative model for the integration of multimodal data. Nature Methods. 20(8). 1222–1231. 118 indexed citations breakdown →
7.
Lopez, Romain, Baoguo Li, Hadas Keren‐Shaul, et al.. (2022). DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology. 40(9). 1360–1369. 118 indexed citations
8.
Kreimer, Anat, Tal Ashuach, Fumitaka Inoue, et al.. (2022). Massively parallel reporter perturbation assays uncover temporal regulatory architecture during neural differentiation. Nature Communications. 13(1). 1504–1504. 27 indexed citations
9.
Hughes, J. Weston, et al.. (2021). Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning. Nucleic Acids Research. 49(19). e110–e110. 3 indexed citations
10.
Gaidt, Moritz M., et al.. (2021). Self-guarding of MORC3 enables virulence factor-triggered immunity. Nature. 600(7887). 138–142. 43 indexed citations
11.
Quinn, Jeffrey J., Matthew G. Jones, Ross A. Okimoto, et al.. (2021). Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science. 371(6532). 183 indexed citations
12.
13.
Gayoso, Adam, Zoë Steier, Romain Lopez, et al.. (2021). Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods. 18(3). 272–282. 249 indexed citations breakdown →
14.
Svensson, Valentine, Adam Gayoso, Nir Yosef, & Lior Pachter. (2020). Interpretable factor models of single-cell RNA-seq via variational autoencoders. Bioinformatics. 36(11). 3418–3421. 98 indexed citations
15.
Afik, Shaked, et al.. (2019). Reconstructing B-cell receptor sequences from short-read single-cell RNA sequencing with BRAPeS. Life Science Alliance. 2(4). e201900371–e201900371. 9 indexed citations
16.
Lopez, Romain, Jeffrey Regier, Michael I. Jordan, & Nir Yosef. (2018). Information Constraints on Auto-Encoding Variational Bayes. arXiv (Cornell University). 31. 6114–6125. 7 indexed citations
17.
Kishi, Yasuhiro, Takaaki Kondo, Sheng Xiao, et al.. (2016). Protein C receptor (PROCR) is a negative regulator of Th17 pathogenicity. The Journal of Experimental Medicine. 213(11). 2489–2501. 49 indexed citations
18.
Shalek, Alex K., Jellert T. Gaublomme, Lili Wang, et al.. (2012). Nanowire-Mediated DeliveryEnables Functional Interrogationof Primary Immune Cells: Application to the Analysis of Chronic LymphocyticLeukemia. Europe PMC (PubMed Central). 142 indexed citations
19.
Yosef, Nir, Martin Kupiec, Eytan Ruppin, & Roded Sharan. (2009). A complex-centric view of protein network evolution. Nucleic Acids Research. 37(12). e88–e88. 21 indexed citations
20.
Freilich, Shiri, Anat Kreimer, Elhanan Borenstein, et al.. (2009). Metabolic-network-driven analysis of bacterial ecological strategies. Genome biology. 10(6). R61–R61. 82 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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