Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning

Van Hoan Do, Stefan Canzar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods.

Original languageEnglish (US)
Title of host publication15th International Conference on Knowledge and Systems Engineering, KSE 2023 - Proceedings
EditorsHuynh Thi Thanh Binh, Van Thuc Hoang, Le Minh Nguyen, Sy Vinh Le, Thi Dao Vu, Duy Trung Pham
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350329742
StatePublished - 2023
Event15th International Conference on Knowledge and Systems Engineering, KSE 2023 - Virtual, Online, Viet Nam
Duration: Oct 18 2023Oct 20 2023

Publication series

NameProceedings - International Conference on Knowledge and Systems Engineering, KSE
ISSN (Electronic)2694-4804


Conference15th International Conference on Knowledge and Systems Engineering, KSE 2023
Country/TerritoryViet Nam
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

Cite this