Portrait of Jun Ding

Jun Ding

Affiliate Member
Assistant professor, McGill University, Department of Medicine
Research Topics
Computational Biology
Medical Machine Learning
Representation Learning
Website
Google Scholar

Biography

Jun Ding is an assistant professor in the Department of Medicine of the Faculty of Medicine and Health Sciences at McGill University.

Alongside his team, he is dedicated to employing machine learning techniques to decipher the complex dynamics of cells in various diseases, such as developmental disorders, pulmonary diseases and cancers. The diverse and intricate nature of these conditions necessitates innovative approaches, prompting the use of state-of-the-art single-cell technologies to meticulously profile individual cell states. The result is a rich source of data for our machine learning models.

These technologies present unprecedented opportunities to advance understanding, particularly in fields like developmental and cancer biology. However, the challenge is to develop computational models capable of linking this intricate biomedical data to potential discoveries.

Ding’s primary focus lies in the development and refinement of machine learning methodologies, especially probabilistic graphical models, to effectively analyze, model and visualize both single-cell and bulk omics data, often featuring longitudinal or spatial dimensions. The goal is to harness these advanced machine learning techniques to deepen the comprehension of cellular dynamics, and so develop groundbreaking diagnostic and therapeutic strategies that can significantly benefit public health.

Current Students

Vishvak Raghavan
PhD - McGill University
Principal supervisor :
Yue Li
Github

Publications

BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.
Kim A. Tran
Erwan Pernet
Mina Sadeghi
Jeffrey Downey
Julia Chronopoulos
Elizabeth Lapshina
Oscar Tsai
Eva Kaufmann
Jun Ding
Maziar Divangahi
2024-01-14
Nature Immunology (published)
doi.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
Yu Li
Jun Ding
Yue Li
Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) has emerged as a powerful technique for investigating op… (see more)en chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remain challenging due to its sparsity and noise. Genome Foundation Models (GFMs), pre-trained on massive DNA sequences, have proven effective at genome analysis. Given that open chromatin regions (OCRs) harbour salient sequence features, we hypothesize that leveraging GFMs’ sequence embeddings can improve the accuracy and generalizability of scATAC-seq modeling. Here, we introduce the Genome Foundation Embedded Topic Model (GFETM), an interpretable deep learning framework that combines GFMs with the Embedded Topic Model (ETM) for scATAC-seq data analysis. By integrating the DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state specific TF activity both with zero-shot inference and attention mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes.
2023-12-31
Lecture Notes in Computer Science (published)
doi.org
Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases
Jun Ding
Yumin Zheng
Jonas C. Schupp
Taylor S Adams
Geremy Clair
Aurelien Justet
Farida Ahangari
Xiting Yan
Paul Hansen
Marianne Carlon
Emanuela Cortesi
Marie Vermant
Robin Vos
De Sadeleer J Laurens
Ivan O Rosas
Ricardo Pineda
John Sembrat
Melanie Königshoff
John E McDonough
Bart M. Vanaudenaerde … (see 2 more)
Wim A Wuyts
Naftali Kaminski
Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap rem… (see more)ains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI’s cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI’s predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.
2023-12-17
Research Square (published)
doi.org
scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data
Yiming Jia
Hao Wu
Jun Ding
2023-11-26
bioRxiv (preprint)
doi.org
scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers
Mingyang Li
Yanshuo Chen
Jun Ding
2023-11-22
bioRxiv (preprint)
doi.org
Unveiling the Impact of Arsenic Toxicity on Immune Cells in Atherosclerotic Plaques: Insights from Single-Cell Multi-Omics Profiling
Kiran Makhani
Xiuhui Yang
France Dierick
Nivetha Subramaniam
Natascha Gagnon
Talin Ebrahimian
Hao Wu
Jun Ding
Koren K. Mann
2023-11-22
bioRxiv (preprint)
doi.org
scCobra: Contrastive cell embedding learning with domain-adaptation for single-cell data integration and harmonization
Bowen Zhao
Dong-Qing Wei
Yi Xiong
Jun Ding
The rapid development of single-cell technologies has underscored the need for more effective methods in the integration and harmonization o… (see more)f single-cell sequencing data. The prevalent challenge of batch effects, resulting from technical and biological variations across studies, demands accurate and reliable solutions for data integration. Traditional tools often have limitations, both due to reliance on gene expression distribution assumptions and the common issue of over-correction, particularly in methods based on anchor alignments. Here we introduce scCobra, a deep neural network tool designed specifically to address these challenges. By leveraging a deep generative model that combines a contrastive neural network with domain adaptation, scCobra effectively mitigates batch effects and minimizes over-correction without depending on gene expression distribution assumptions. Additionally, scCobra enables online label transfer across datasets with batch effects, facilitating the continuous integration of new data without retraining, and offers features for batch effect simulation and advanced multi-omic batch integration. These capabilities make scCobra a versatile data integration and harmonization tool for achieving accurate and insightful biological interpretations from complex datasets.
2022-10-23
bioRxiv (preprint)
doi.org