Repository: Freie Universität Berlin, Math Department

A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data

Wang, Yuwei and Lian, Bin and Zhang, Haohui and Zhong, Yuanke and He, Jie and Wu, Fashuai and Reinert, Knut and Shang, Xuequn and Yang, Hui and Hu, Jialu and Mathelier, Anthony (2023) A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data. Bioinformatics, 39 (1). ISSN 1367-4803

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Motivation: Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. Results: Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. Availability and implementation: The VIMCCA algorithm has been implemented in our toolkit package scbean (⁠≥0.5.0), and its code has been archived at under MIT license.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2948
Deposited By: Anja Kasseckert
Deposited On:19 Apr 2023 13:29
Last Modified:19 Apr 2023 13:29

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