Nir Yosef
Impact in
- Immunology top 0.2%
- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Immune cells in cancer
- Immunotherapy and Immune Responses
- Biophysics top 0.1%
- Cell Image Analysis Techniques
Papers in
- Immunology 60
- Immune Cell Function and Interaction 33
- T-cell and B-cell Immunology 31
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- Single-cell and spatial transcriptomics 59
- Genomics and Chromatin Dynamics 17
- Gene Regulatory Network Analysis 14
- Bioinformatics and Genomic Networks 13
- Co-authors
- Aviv Regev (21 shared papers)Romain Lopez (6 shared papers)Jeffrey Regier (4 shared papers)Michael I. Jordan (7 shared papers)Michael B. Cole (6 shared papers)Davide Risso (4 shared papers)John Ngai (4 shared papers)Elizabeth Purdom (4 shared papers)
- Journals
- Cell (11 papers)Nature Methods (8 papers)Nucleic Acids Research (7 papers)Nature (7 papers)Genome biology (6 papers)
- Partner nations
- United StatesIsraelGermany
In The Last Decade
Nir Yosef
151 papers receiving 22.1k citations
Nir Yosef's Hit Papers
Peers
Comparison fields: 5 of 168
- Immunology 7.2k
- Biophysics 1.4k
- Molecular Biology 12.2k
- Cancer Research 2.3k
- Oncology 2.5k
Countries citing papers authored by Nir Yosef
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
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-authors
The 25 scholars most cited alongside Nir Yosef, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 153 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics Hit paper breakdown → | 2018 | 1908 |
| 2 | Deep generative modeling for single-cell transcriptomics Hit paper breakdown → | 2018 | 1376 |
| 3 | Sodium chloride drives autoimmune disease by the induction of pathogenic TH17 cells Hit paper breakdown → | 2013 | 1150 |
| 4 | Induction and molecular signature of pathogenic TH17 cells Hit paper breakdown → | 2012 | 996 |
| 5 | Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells Hit paper breakdown → | 2013 | 901 |
| 6 | Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1 Hit paper breakdown → | 2013 | 884 |
| 7 | The epigenetic landscape of T cell exhaustion Hit paper breakdown → | 2016 | 774 |
| 8 | Single-cell RNA-seq reveals dynamic paracrine control of cellular variation Hit paper breakdown → | 2014 | 771 |
| 9 | Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis Hit paper breakdown → | 2011 | 743 |
| 10 | The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans Hit paper breakdown → | 2022 | 691 |
| 11 | Cross-tissue immune cell analysis reveals tissue-specific features in humans Hit paper breakdown → | 2022 | 638 |
| 12 | Dynamic regulatory network controlling TH17 cell differentiation Hit paper breakdown → | 2013 | 556 |
| 13 | 2015 | 477 | |
| 14 | Revealing the vectors of cellular identity with single-cell genomics Hit paper breakdown → | 2016 | 434 |
| 15 | A Python library for probabilistic analysis of single-cell omics data Hit paper breakdown → | 2022 | 399 |
| 16 | Metabolic modeling of single Th17 cells reveals regulators of autoimmunity Hit paper breakdown → | 2021 | 359 |
| 17 | Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models Hit paper breakdown → | 2021 | 328 |
| 18 | Joint probabilistic modeling of single-cell multi-omic data with totalVI Hit paper breakdown → | 2021 | 325 |
| 19 | 2015 | 321 | |
| 20 | Mapping single-cell data to reference atlases by transfer learning Hit paper breakdown → | 2021 | 318 |
About Nir Yosef
Nir Yosef is a scholar working on Immunology, Molecular Biology, Biophysics, Cancer Research and Oncology, having authored 153 papers that have together received 22.3k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (59 papers), Immune Cell Function and Interaction (33 papers), T-cell and B-cell Immunology (31 papers), Genomics and Chromatin Dynamics (17 papers), Cell Image Analysis Techniques (16 papers), Cancer Genomics and Diagnostics (14 papers), Gene Regulatory Network Analysis (14 papers) and Bioinformatics and Genomic Networks (13 papers). The work is most often cited by research in Immunology (7.2k citations), Biophysics (1.4k citations), Molecular Biology (12.2k citations), Cancer Research (2.3k citations) and Oncology (2.5k citations). Nir Yosef has collaborated with scholars based in United States, Israel and Germany. Frequent co-authors include Aviv Regev, Romain Lopez, Jeffrey Regier, Michael I. Jordan, Michael B. Cole, Davide Risso, John Ngai, Elizabeth Purdom, Sandrine Dudoit and Vijay Kumar Kuchroo. Their work appears in journals such as Cell, Nature Methods, Nucleic Acids Research, Nature and Genome biology.
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.