Author

E. So

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

Biology Disciplines

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Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 2 Assay:bulk RNA-seq· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

B. Haibe-Kains· 1 shared B. Soskic· 1 shared B. Wang· 1 shared C. G. C. Larminie· 1 shared D. F. Tough· 1 shared D. J. Smyth· 1 shared D. Wille· 1 shared E. C. Gamez· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperCell Biology

B. Haibe-Kains

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2 papers

Recent·Most cited

GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

E. So, S. Hayat, S. Kadambat Nair, B. Wang, B. Haibe-Kains

GraphComm predicts cell-cell communication in single-cell RNAseq data by integrating gene expression with protein interaction networks and spatial information.

bioRxiv · Apr 26, 2023 · PreprintCell Biology

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

E. C. Gamez, B. Soskic, T. I. Roumeliotis, E. So, D. J. Smyth, M. Baldrighi, D. Wille, N. Nakic, J. Esparza-Gordillo, C. G. C. Larminie, P. G. Bronson, D. F. Tough, W. C. Rowan, J. S. Choudhary, G. Trynka

The paper uses single-cell RNA-seq and other methods to show that CD4+ T cells form an effectorness gradient, with cytokine responses varying along this continuum.

bioRxiv · Sep 4, 2019Immunology