Dana Pe’er
Impact in
- Immunology top 0.1%
- Immune Cell Function and Interaction
- Immune cells in cancer
- Biophysics top 0.05%
Papers in
-
- Single-cell and spatial transcriptomics 56
- Gene Regulatory Network Analysis 19
- Gene expression and cancer classification 14
- Bioinformatics and Genomic Networks 14
- Immunology 35
- T-cell and B-cell Immunology 17
- Immune Cell Function and Interaction 14
- Co-authors
- Nir Friedman (5 shared papers)Garry P. Nolan (12 shared papers)Michal Linial (2 shared papers)Iftach Nachman (2 shared papers)Sean C. Bendall (9 shared papers)Erin F. Simonds (6 shared papers)Jacob Levine (7 shared papers)Karen Sachs (4 shared papers)
- Journals
- Cell (18 papers)Nature (8 papers)Nature Biotechnology (8 papers)Science (8 papers)Proceedings of the National Academy of Sciences (6 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Dana Pe’er
137 papers receiving 34.6k citations
Dana Pe’er's Hit Papers
Peers
Comparison fields: 5 of 204
- Immunology 8.6k
- Biophysics 2.3k
- Oncology 7.3k
- Cancer Research 4.0k
- Molecular Biology 18.6k
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 139 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 | 2392 |
| 2 | Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum Hit paper breakdown → | 2011 | 1906 |
| 3 | SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues Hit paper breakdown → | 2020 | 1798 |
| 4 | Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis Hit paper breakdown → | 2015 | 1581 |
| 5 | Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment Hit paper breakdown → | 2018 | 1494 |
| 6 | viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia Hit paper breakdown → | 2013 | 1262 |
| 7 | Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data Hit paper breakdown → | 2003 | 1253 |
| 8 | Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data Hit paper breakdown → | 2005 | 1215 |
| 9 | Chromosomal instability drives metastasis through a cytosolic DNA response Hit paper breakdown → | 2018 | 1202 |
| 10 | Recovering Gene Interactions from Single-Cell Data Using Data Diffusion Hit paper breakdown → | 2018 | 1128 |
| 11 | Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade Hit paper breakdown → | 2017 | 1077 |
| 12 | Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses Hit paper breakdown → | 2017 | 963 |
| 13 | An Immune Atlas of Clear Cell Renal Cell Carcinoma Hit paper breakdown → | 2017 | 794 |
| 14 | Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development Hit paper breakdown → | 2014 | 684 |
| 15 | Toward understanding and exploiting tumor heterogeneity Hit paper breakdown → | 2015 | 613 |
| 16 | Normalization of mass cytometry data with bead standards Hit paper breakdown → | 2013 | 590 |
| 17 | Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity Hit paper breakdown → | 2019 | 467 |
| 18 | Characterization of cell fate probabilities in single-cell data with Palantir Hit paper breakdown → | 2019 | 467 |
| 19 | Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm Hit paper breakdown → | 2015 | 456 |
| 20 | An integrated cell atlas of the lung in health and disease Hit paper breakdown → | 2023 | 425 |
About Dana Pe’er
Dana Pe’er is a scholar working on Molecular Biology, Immunology, Oncology, Biophysics and Cancer Research, having authored 139 papers that have together received 35.2k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (56 papers), Gene Regulatory Network Analysis (19 papers), Cell Image Analysis Techniques (17 papers), T-cell and B-cell Immunology (17 papers), Cancer Genomics and Diagnostics (15 papers), Immune Cell Function and Interaction (14 papers), Gene expression and cancer classification (14 papers) and Bioinformatics and Genomic Networks (14 papers). The work is most often cited by research in Immunology (8.6k citations), Biophysics (2.3k citations), Oncology (7.3k citations), Cancer Research (4.0k citations) and Molecular Biology (18.6k citations). Dana Pe’er has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Nir Friedman, Garry P. Nolan, Michal Linial, Iftach Nachman, 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, Nature Biotechnology, Science and Proceedings of the National Academy of Sciences.
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.