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Dylan Mann-Krzisnik

Doctorat - McGill
Superviseur⋅e principal⋅e
Yue Li
Sujets de recherche
Apprentissage multimodal
Biologie computationnelle

Publications

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data
Dylan Mann-Krzisnik
Yue Li
Integrating multimodal single-cell data, such as scRNA-seq and scATAC-seq, is key for decoding gene regulatory networks but remains challeng… (voir plus)ing due to issues like feature harmonization and limited quantity of paired data. To address these challenges, we introduce ECLARE, a novel framework combining multi-teacher ensemble knowledge distillation with contrastive learning for diagonal integration of single-cell multi-omic data. ECLARE trains teacher models on paired datasets to guide a student model for unpaired data, leveraging a refined contrastive objective and transport-based loss for precise cross-modality alignment. Experiments demonstrate ECLARE’s competitive performance in cell pairing accuracy, multimodal integration and biological structure preservation, indicating that multi-teacher knowledge distillation provides an effective mean to improve a diagonal integration model beyond its zero-shot capabilities. Additionally, we validate ECLARE’s applicability through a case study on major depressive disorder (MDD) data, illustrating its capability to reveal gene regulatory insights from unpaired nuclei. While current results highlight the potential of ensemble distillation in multi-omic analyses, future work will focus on optimizing model complexity, dataset scalability, and exploring applications in diverse multi-omic contexts. ECLARE establishes a robust foundation for biologically informed single-cell data integration, facilitating advanced downstream analyses and scaling multi-omic data for training advanced machine learning models.
2025-01-27
bioRxiv (prépublication)
doi.org
Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Yi Wang
Yue Li
2024-05-14
STAR Protocols (publié)
doi.org
Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Yi Wang
Yue Li
The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individua… (voir plus)l cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human bone marrow mononuclear cells (BMMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.
2023-01-31
bioRxiv (prépublication)
doi.org