Graham Su

2.7k total citations · 4 hit papers
11 papers, 1.3k citations indexed

About

Graham Su is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Graham Su has authored 11 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Immunology and 2 papers in Cancer Research. Recurrent topics in Graham Su's work include Single-cell and spatial transcriptomics (10 papers), Cancer Genomics and Diagnostics (2 papers) and Gene expression and cancer classification (2 papers). Graham Su is often cited by papers focused on Single-cell and spatial transcriptomics (10 papers), Cancer Genomics and Diagnostics (2 papers) and Gene expression and cancer classification (2 papers). Graham Su collaborates with scholars based in United States, Sweden and China. Graham Su's co-authors include Rong Fan, Yanxiang Deng, Yang Liu, Archibald Enninful, Di Zhang, Zhiliang Bai, Stephanie Halene, Yang Xiao, Mingyu Yang and Dongjoo Kim and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Graham Su

9 papers receiving 1.2k citations

Hit Papers

High-Spatial-Resolution Multi-Omics Sequencing via Determ... 2020 2026 2022 2024 2020 2022 2022 2023 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Graham Su United States 8 1.1k 249 215 162 117 11 1.3k
Archibald Enninful United States 6 863 0.8× 196 0.8× 160 0.7× 139 0.9× 97 0.8× 8 1.0k
Zachary Chiang United States 7 1.2k 1.1× 361 1.4× 164 0.8× 154 1.0× 137 1.2× 12 1.4k
Michaela Asp Sweden 6 1.0k 1.0× 205 0.8× 225 1.0× 164 1.0× 83 0.7× 8 1.2k
Andrew Earl United States 7 908 0.9× 213 0.9× 116 0.5× 146 0.9× 71 0.6× 10 1.0k
Luyi Tian Australia 14 715 0.7× 189 0.8× 169 0.8× 116 0.7× 66 0.6× 17 912
Mingyu Yang China 5 519 0.5× 121 0.5× 119 0.6× 86 0.5× 63 0.5× 9 626
Rasa Elmentaite United Kingdom 6 601 0.6× 127 0.5× 233 1.1× 96 0.6× 108 0.9× 7 790
Anna Arutyunyan United States 8 570 0.5× 134 0.5× 220 1.0× 74 0.5× 125 1.1× 10 825
Katy Vandereyken Belgium 3 457 0.4× 112 0.4× 81 0.4× 82 0.5× 66 0.6× 3 660

Countries citing papers authored by Graham Su

Since Specialization
Citations

This map shows the geographic impact of Graham Su'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 Graham Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Graham Su more than expected).

Fields of papers citing papers by Graham Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Graham Su. 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 Graham Su. The network helps show where Graham Su may publish in the future.

Co-authorship network of co-authors of Graham Su

This figure shows the co-authorship network connecting the top 25 collaborators of Graham Su. A scholar is included among the top collaborators of Graham Su based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Graham Su. Graham Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Enninful, Archibald, Mingyu Yang, Zhiliang Bai, et al.. (2026). Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus. Nature Methods.
2.
Tan, Yuqi, Martin Becker, Maximilian Haist, et al.. (2025). SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis. Nature Communications. 16(1). 10652–10652.
3.
Bai, Zhiliang, Dingyao Zhang, Yan Gao, et al.. (2024). Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues. Cell. 187(23). 6760–6779.e24. 34 indexed citations
4.
Liu, Yang, Marcello DiStasio, Graham Su, et al.. (2023). High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nature Biotechnology. 41(10). 1405–1409. 184 indexed citations breakdown →
5.
Deng, Yanxiang, Marek Bartošovič, Petra Kukanja, et al.. (2022). Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science. 375(6581). 681–686. 207 indexed citations breakdown →
6.
Deng, Yanxiang, Marek Bartošovič, Sai Ma, et al.. (2022). Spatial profiling of chromatin accessibility in mouse and human tissues. Nature. 609(7926). 375–383. 199 indexed citations breakdown →
7.
Xiao, Yong, Mengjie Zhao, Yanxiang Deng, et al.. (2022). Single-Cell Transcriptomics Revealed Subtype-Specific Tumor Immune Microenvironments in Human Glioblastomas. Frontiers in Immunology. 13. 914236–914236. 45 indexed citations
8.
Su, Graham, Xiaoyu Qin, Archibald Enninful, et al.. (2021). Spatial multi-omics sequencing for fixed tissue via DBiT-seq. STAR Protocols. 2(2). 100532–100532. 35 indexed citations
9.
Bai, Zhiliang, Graham Su, & Rong Fan. (2021). Single-Cell Analysis Technologies for Immuno-Oncology Research: From Mechanistic Delineation to Biomarker Discovery. Genomics Proteomics & Bioinformatics. 19(2). 191–207. 7 indexed citations
10.
Liu, Yang, Mingyu Yang, Yanxiang Deng, et al.. (2020). High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell. 183(6). 1665–1681.e18. 540 indexed citations breakdown →
11.
Liu, Yang, Mingyu Yang, Yanxiang Deng, et al.. (2019). High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue. SSRN Electronic Journal. 9 indexed citations

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.

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