Jeffrey M. Granja
- Immunology top 2%
- T-cell and B-cell Immunology 4
- Immune Cell Function and Interaction 4
- Immunotherapy and Immune Responses 3
- Oncology top 2%
- Cancer Research top 5%
- Cancer Genomics and Diagnostics 3
- Molecular Biology top 5%
- Single-cell and spatial transcriptomics 4
- Genomics and Chromatin Dynamics 3
- RNA modifications and cancer 2
- CRISPR and Genetic Engineering 2
- Biophysics top 5%
- Co-authors
- Howard Y. ChangWilliam J. GreenleafM. Ryan CorcesSarah E. PierceAnsuman T. SatpathyS. Tansu BagdatliHani ChoudhryKathryn E. Yost
- Cited by
- ImmunologyOncologyCancer Research
- Journals
- Nature (1 paper)Proceedings of the National Academy of Sciences (2 papers)Nature Medicine (1 paper)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Jeffrey M. Granja
17 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Immunology 1.1k
- Oncology 1.3k
- Cancer Research 469
- Molecular Biology 1.7k
- Biophysics 106
Countries citing papers authored by Jeffrey M. Granja
This map shows the geographic impact of Jeffrey M. Granja'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 Jeffrey M. Granja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey M. Granja more than expected).
Fields of papers citing papers by Jeffrey M. Granja
This network shows the impact of papers produced by Jeffrey M. Granja. 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 Jeffrey M. Granja. The network helps show where Jeffrey M. Granja may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jeffrey M. Granja, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 58 | |
| 2 | 2021 | 34 | |
| 3 | 2021 | 94 | |
| 4 | ArchR is a scalable software package for integrative single-cell chromatin accessibility analysisbreakdown → | 2021 | 561 |
| 5 | 2020 | 23 | |
| 6 | 2020 | 2 | |
| 7 | 2020 | 198 | |
| 8 | 2020 | 28 | |
| 9 | 2019 | 79 | |
| 10 | 2019 | 64 | |
| 11 | c-Jun overexpression in CAR T cells induces exhaustion resistancebreakdown → | 2019 | 514 |
| 12 | 2019 | 257 | |
| 13 | Clonal replacement of tumor-specific T cells following PD-1 blockadebreakdown → | 2019 | 876 |
| 14 | 2019 | 1 | |
| 15 | 2018 | 104 | |
| 16 | 2018 | 11 | |
| 17 | 2017 | 133 |
About Jeffrey M. Granja
Jeffrey M. Granja is a scholar working on Cancer Research, Immunology and Molecular Biology, having authored 17 papers that have together received 3.0k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (4 papers), T-cell and B-cell Immunology (4 papers), Immune Cell Function and Interaction (4 papers), Immunotherapy and Immune Responses (3 papers), Cancer Genomics and Diagnostics (3 papers), Genomics and Chromatin Dynamics (3 papers), RNA modifications and cancer (2 papers) and CRISPR and Genetic Engineering (2 papers). The work is most often cited by research in Immunology (1.1k citations), Oncology (1.3k citations) and Cancer Research (469 citations). Jeffrey M. Granja has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Howard Y. Chang, William J. Greenleaf, M. Ryan Corces, Sarah E. Pierce, Ansuman T. Satpathy, S. Tansu Bagdatli, Hani Choudhry, Kathryn E. Yost, Yanyan Qi and Anne Lynn S. Chang. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Medicine.
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.