Author

Jesse M. Zhang

Stanford UniversityORCID 0000-0002-9970-0693Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 3 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

David N. C. TseiD· 2 shared Aidan F. WintersiD· 1 shared Airol A. Ubas· 1 shared Ajay Sapre· 1 shared Alexander Rosenberg· 1 shared Bryan HariadiiD· 1 shared Charles Roco· 1 shared Crina Curca Alec Salvino· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

David N. C. TseORCID

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Author profile →

3 papers

Recent·Most cited

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Jesse M. Zhang, Airol A. Ubas, Richard de Borja, Valentine Svensson, Nicole Thomas, Neha Thakar, Ian K. Lai, Aidan F. Winters, Umair Khan, Matthew G. Jones, John D. Thompson, Vuong Tran, Joseph Pangallo, Efthymia Papalexi, Ajay Sapre, , Johnny X. Yu

A 100-million-cell atlas: 50 cancer lines pooled as spheroid villages, treated with 379 drugs at several doses, profiled by combinatorial-barcoding scRNA-seq and embedded with a 10-dimensional scVI model.

bioRxiv · Feb 20, 2025 · PreprintGenomics✦ In-depth digest

Valid post-clustering differential analysis for single-cell RNA-Seq

Jesse M. Zhang, Govinda M. Kamath, David N. C. Tse

The paper introduces a framework to correct differential expression analysis after clustering in single-cell RNA-Seq to avoid false discoveries caused by clustering-induced biases.

Cell Systems · Jan 1, 2019Peer-reviewedBioinformatics

An Interpretable Framework for Clustering Single-Cell RNA-Seq Datasets

Jesse M. Zhang, Jue Fan, H. Christina Fan, David Rosenfeld, David N. C. Tse

DendroSplit provides an interpretable clustering framework for single-cell RNA-Seq datasets, improving clarity and reducing subjectivity in clustering results.

BMC Bioinformatics · Mar 9, 2018Peer-reviewedBioinformatics