Author

Ronald Xie

University of TorontoORCID 0000-0002-2184-1656Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Approach:deep learning· 3 Research Focus:dimensionality reduction· 3 Organism:human· 3 Assay:electronic health records· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1

Frequent Co-Authors

Gary D. BaderiD· 2 shared Xing ChengiD· 2 shared Ali DarbandiiD· 1 shared Andrew ZhangiD· 1 shared Ben MulcahyiD· 1 shared Bo WangiD· 1 shared Bobby RanjaniD· 1 shared Chloe Xueqi WangiD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersCell Biology

Gary D. BaderORCID

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Author profile →

4 papers

Recent·Most cited

Multiscale volume electron microscopy of the human liver maps vascular-cellular architecture, organelle dynamics and inter-organelle communication

Xing Cheng, Ronald Xie, Ben Mulcahy, Ali Darbandi, Cornelia E. Thoeni, Ishaan Singh Chandok, Y Lee, Jun Ma, Fez Ali, Ian D. McGilvray, Sonya A. MacParland, Mei Zhen, Gary D. Bader

The paper maps the multiscale structure of human liver tissue using electron microscopy and deep learning, revealing vascular, cellular, and organelle organization and dynamics.

bioRxiv · Apr 24, 2026 · PreprintCell Biology

RETINA: Reconstruction-based Pre-Trained Enhanced TransUNet for Electron Microscopy Segmentation on the CEM500K Dataset

Cheng Xing, Ronald Xie, Gary D. Bader

RETINA improves EM image segmentation by combining pre-training on CEM500K with a hybrid model, outperforming existing methods on five public datasets.

PLoS Computational Biology · May 28, 2025Peer-reviewedBioinformatics

scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics

Chloe Xueqi Wang, Haotian Cui, Andrew Zhang, Ronald Xie, Hani Goodarzi, Bo Wang

Further pretraining a single-cell foundation model on 30 million spatial profiles, with a protocol-aware mixture-of-experts decoder and a neighbourhood objective, supports multi-slide integration, deconvolution and imputation.

bioRxiv · Feb 5, 2025 · PreprintBioinformatics

DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

Bobby Ranjan, Wenjie Sun, Jinyu Park, Kunal Mishra, Florian Schmidt, Ronald Xie, Fatemeh Alipour, Vipul Singhal, Ignasius Joanito, Mohammad Amin Honardoost, Jacy Mei Yun Yong, Ee Tzun Koh, Khai Pang Leong, Nirmala Arul Rayan, Michelle Gek Liang Lim, Shyam Prabhakar

DUBStepR improves single-cell clustering by selecting features using gene-gene correlations and a Density Index, outperforming existing methods and detecting rare cell populations.

Nature Communications · Oct 6, 2021Peer-reviewedBioinformatics