Sarah A. Teichmann
Impact in
- Molecular Biology top 0.05%
- Single-cell and spatial transcriptomics
- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- RNA and protein synthesis mechanisms
- Gene expression and cancer classification
- Genomics and Phylogenetic Studies
- Biophysics top 0.05%
Papers in
-
- Single-cell and spatial transcriptomics 95
- Protein Structure and Dynamics 45
- RNA and protein synthesis mechanisms 40
- Genomics and Phylogenetic Studies 28
- Bioinformatics and Genomic Networks 26
- Genomics and Chromatin Dynamics 23
- Immunology 69
- T-cell and B-cell Immunology 33
- Immune Cell Function and Interaction 31
- Co-authors
- John C. Marioni (16 shared papers)Roser Vento‐Tormo (6 shared papers)Valentine Svensson (11 shared papers)Mirjana Efremova (7 shared papers)M. Madan Babu (8 shared papers)Nicholas M. Luscombe (4 shared papers)Oliver Stegle (6 shared papers)Joseph A. Marsh (16 shared papers)
- Journals
- Nature Communications (18 papers)Nature Methods (12 papers)Journal of Molecular Biology (12 papers)Science (10 papers)Current Opinion in Structural Biology (9 papers)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Sarah A. Teichmann
262 papers receiving 32.1k citations
Sarah A. Teichmann's Hit Papers
Peers
Comparison fields: 5 of 209
- Molecular Biology 23.6k
- Biophysics 1.9k
- Immunology 6.4k
- Cancer Research 3.8k
- Aging 257
Countries citing papers authored by Sarah A. Teichmann
This map shows the geographic impact of Sarah A. Teichmann'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 Sarah A. Teichmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah A. Teichmann more than expected).
Fields of papers citing papers by Sarah A. Teichmann
This network shows the impact of papers produced by Sarah A. Teichmann. 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 Sarah A. Teichmann. The network helps show where Sarah A. Teichmann may publish in the future.
Co-authors
The 25 scholars most cited alongside Sarah A. Teichmann, 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 264 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes Hit paper breakdown → | 2020 | 1792 |
| 2 | Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti–PD-L1 immunotherapy in pancreatic cancer Hit paper breakdown → | 2013 | 1490 |
| 3 | A census of human transcription factors: function, expression and evolution Hit paper breakdown → | 2009 | 1137 |
| 4 | The Human Cell Atlas Hit paper breakdown → | 2017 | 1081 |
| 5 | The Technology and Biology of Single-Cell RNA Sequencing Hit paper breakdown → | 2015 | 964 |
| 6 | A rapid and robust method for single cell chromatin accessibility profiling Hit paper breakdown → | 2018 | 860 |
| 7 | Computational and analytical challenges in single-cell transcriptomics Hit paper breakdown → | 2015 | 816 |
| 8 | Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells Hit paper breakdown → | 2015 | 775 |
| 9 | Genomic analysis of regulatory network dynamics reveals large topological changes Hit paper breakdown → | 2004 | 680 |
| 10 | Accounting for technical noise in single-cell RNA-seq experiments Hit paper breakdown → | 2013 | 676 |
| 11 | A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications Hit paper breakdown → | 2017 | 668 |
| 12 | Exponential scaling of single-cell RNA-seq in the past decade Hit paper breakdown → | 2018 | 572 |
| 13 | Structure and evolution of transcriptional regulatory networks Hit paper breakdown → | 2004 | 547 |
| 14 | BBKNN: fast batch alignment of single cell transcriptomes Hit paper breakdown → | 2019 | 422 |
| 15 | Classification of low quality cells from single-cell RNA-seq data Hit paper breakdown → | 2016 | 421 |
| 16 | 2003 | 421 | |
| 17 | 2004 | 382 | |
| 18 | 2008 | 382 | |
| 19 | 2017 | 381 | |
| 20 | 2001 | 380 |
About Sarah A. Teichmann
Sarah A. Teichmann is a scholar working on Molecular Biology, Immunology, Biophysics, Genetics and Materials Chemistry, having authored 264 papers that have together received 32.5k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (95 papers), Protein Structure and Dynamics (45 papers), RNA and protein synthesis mechanisms (40 papers), T-cell and B-cell Immunology (33 papers), Immune Cell Function and Interaction (31 papers), Genomics and Phylogenetic Studies (28 papers), Bioinformatics and Genomic Networks (26 papers) and Genomics and Chromatin Dynamics (23 papers). The work is most often cited by research in Molecular Biology (23.6k citations), Biophysics (1.9k citations), Immunology (6.4k citations), Cancer Research (3.8k citations) and Aging (257 citations). Sarah A. Teichmann has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include John C. Marioni, Roser Vento‐Tormo, Valentine Svensson, Mirjana Efremova, M. Madan Babu, Nicholas M. Luscombe, Oliver Stegle, Joseph A. Marsh, Cyrus Chothia and Sarah Kummerfeld. Their work appears in journals such as Nature Communications, Nature Methods, Journal of Molecular Biology, Science and Current Opinion in Structural 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.