Dana Pe’er
Impact in
- Biophysics top 0.05%
- Cell Image Analysis Techniques
- Immunology top 0.2%
- Immune Cell Function and Interaction
- Immune cells in cancer
Papers in
-
- Single-cell and spatial transcriptomics 47
- Gene Regulatory Network Analysis 19
- Gene expression and cancer classification 16
- Bioinformatics and Genomic Networks 15
- Oncology 30
- Co-authors
- Nir Friedman (7 shared papers)Garry P. Nolan (12 shared papers)Iftach Nachman (3 shared papers)Michal Linial (2 shared papers)Sean C. Bendall (9 shared papers)Erin F. Simonds (6 shared papers)Jacob Levine (8 shared papers)Karen Sachs (4 shared papers)
- Journals
- Cell (12 papers)Nature Biotechnology (7 papers)Cancer Research (7 papers)Proceedings of the National Academy of Sciences (6 papers)Nature (5 papers)
- Partner nations
- United StatesIsraelCanada
In The Last Decade
Dana Pe’er
117 papers receiving 22.8k citations
Dana Pe’er's Hit Papers
Peers
Comparison fields: 5 of 200
- Biophysics 2.0k
- Immunology 6.1k
- Molecular Biology 14.7k
- Cancer Research 2.9k
- Oncology 5.0k
Countries citing papers authored by Dana Pe’er
This map shows the geographic impact of Dana Pe’er'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 Dana Pe’er with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dana Pe’er more than expected).
Fields of papers citing papers by Dana Pe’er
This network shows the impact of papers produced by Dana Pe’er. 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 Dana Pe’er. The network helps show where Dana Pe’er may publish in the future.
Co-authors
The 25 scholars most cited alongside Dana Pe’er, 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 120 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Using Bayesian Networks to Analyze Expression Data Hit paper breakdown → | 2000 | 2101 |
| 2 | Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum Hit paper breakdown → | 2011 | 1700 |
| 3 | Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis Hit paper breakdown → | 2015 | 1279 |
| 4 | Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment Hit paper breakdown → | 2018 | 1250 |
| 5 | Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data Hit paper breakdown → | 2003 | 1158 |
| 6 | Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data Hit paper breakdown → | 2005 | 1101 |
| 7 | viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia Hit paper breakdown → | 2013 | 1099 |
| 8 | Recovering Gene Interactions from Single-Cell Data Using Data Diffusion Hit paper breakdown → | 2018 | 945 |
| 9 | Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade Hit paper breakdown → | 2017 | 928 |
| 10 | An Immune Atlas of Clear Cell Renal Cell Carcinoma Hit paper breakdown → | 2017 | 694 |
| 11 | Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development Hit paper breakdown → | 2014 | 631 |
| 12 | Normalization of mass cytometry data with bead standards Hit paper breakdown → | 2013 | 462 |
| 13 | Wishbone identifies bifurcating developmental trajectories from single-cell data Hit paper breakdown → | 2016 | 384 |
| 14 | Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity Hit paper breakdown → | 2019 | 374 |
| 15 | 2010 | 360 | |
| 16 | 2015 | 358 | |
| 17 | Characterization of cell fate probabilities in single-cell data with Palantir Hit paper breakdown → | 2019 | 351 |
| 18 | 2001 | 347 | |
| 19 | 1999 | 327 | |
| 20 | Lineage plasticity in cancer: a shared pathway of therapeutic resistance Hit paper breakdown → | 2020 | 310 |
About Dana Pe’er
Dana Pe’er is a scholar working on Molecular Biology, Oncology, Immunology, Cancer Research and Biophysics, having authored 120 papers that have together received 23.2k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (47 papers), Cancer Genomics and Diagnostics (19 papers), Gene Regulatory Network Analysis (19 papers), Gene expression and cancer classification (16 papers), Cell Image Analysis Techniques (15 papers), Bioinformatics and Genomic Networks (15 papers), T-cell and B-cell Immunology (14 papers) and Immune Cell Function and Interaction (12 papers). The work is most often cited by research in Biophysics (2.0k citations), Immunology (6.1k citations), Molecular Biology (14.7k citations), Cancer Research (2.9k citations) and Oncology (5.0k citations). Dana Pe’er has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Nir Friedman, Garry P. Nolan, Iftach Nachman, Michal Linial, Sean C. Bendall, Erin F. Simonds, Jacob Levine, Karen Sachs, Michelle D. Tadmor and El-ad David Amir. Their work appears in journals such as Cell, Nature Biotechnology, Cancer Research, Proceedings of the National Academy of Sciences and Nature.
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.