Author

S. J. Rodig

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Cancer Biology· 5

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Research Focus:cell type annotation· 3 Research Focus:differential expression· 3 Organism:human· 3 Assay:spatial transcriptomics· 3 Approach:statistical model· 3 Approach:machine learning (classical)· 2 Research Focus:spatial domain detection· 2 Approach:benchmark study· 1

Frequent Co-Authors

B. Zhu· 3 shared S. Jiang· 3 shared Y. Bai· 3 shared F. Mahmood· 2 shared G. P. Nolan· 2 shared H. Chen· 2 shared H. Qiu· 2 shared H. Wang· 2 shared

Activity by Year

2022-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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3 shared papersCancer Biology

B. Zhu

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5 papers

Recent·Most cited

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Y. Y. Yeo, H. Qiu, Y. Bai, B. Zhu, Y. Chang, J. Yeung, H. A. Michel, K. T. Wright, M. Shaban, S. Sadigh, D. Nkosi, V. Shanmugam, S. P. T. Yiu, P. Cramer, P. Rock, , S. Jiang

This study uses spatial multi-omics to compare EBV-positive and EBV-negative cHL, identifying distinct TME features and immunological signatures linked to EBV.

bioRxiv · Mar 5, 2024Cancer Biology

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity

A. G. Patel, O. Ashenberg, N. B. Collins, A. Segerstolpe, S. Jiang, M. Slyper, X. Huang, C. Caraccio, H. Jin, H. Sheppard, K. Xu, T.-C. Chang, B. A. Orr, A. Shirinifard, R. H. Chapple, , S. J. Rodig

This paper creates a spatial cell atlas of neuroblastoma, revealing tumor heterogeneity through multi-omics data and identifying spatial compartmentalization and signaling patterns.

bioRxiv · Jan 7, 2024 · PreprintCancer Biology

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

H. Wang, R. Huang, J. Nelson, C. Gao, M. Tran, A. Yeaton, K. Felt, K. L. Pfaff, T. Bowman, S. J. Rodig, K. Wei, B. A. Goods, S. L. Farhi

This study benchmarks three imaging spatial transcriptomics platforms on FFPE tissues, finding 10x Xenium has higher transcript counts without sacrificing specificity, while all platforms align with RNA-seq data but vary in performance metrics.

bioRxiv · Dec 7, 2023Cancer Biology

MAPS: Pathologist-level cell type annotation from tissue images through machine learning

M. Shaban, Y. Bai, H. Qiu, S. Mao, J. Yeung, Y. Y. Yeo, V. Shanmugam, V. Shanmugam, H. Chen, B. Zhu, G. P. Nolan, M. A. Shipp, S. J. Rodig, S. Jiang, F. Mahmood

MAPS provides pathologist-level cell type annotation from spatial proteomics data using machine learning, outperforming existing methods in speed and accuracy.

bioRxiv · Jun 25, 2023 · PreprintCancer Biology

Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section

J.-R. Lin, Y.-A. Chen, D. Campton, J. Cooper, S. Coy, C. Yapp, J. Tefft, E. McCarty, K. Ligon, S. J. Rodig, S. Reese, T. George, S. Santagata, P. K. Sorger

The paper develops a multi-modal imaging platform combining H&E and high-plex immunofluorescence for colorectal cancer diagnosis, showing improved predictive models for progression-free survival.

bioRxiv · Sep 28, 2022Cancer Biology