ArticleCommunications biology2021
Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 17 citations in OpenAlex.
- Identification of Distinct Topological Structures From High-Dimensional Data.bioRxiv : the preprint server for biology · 2025Article
- scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.Biology · 2025Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology.Patterns (New York, N.Y.) · 2023Article
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- CMOT: Cross-Modality Optimal Transport for multimodal inference.Genome biology · 2023Article
- MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology.bioRxiv : the preprint server for biology · 2023Article
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3 authors at 1 institution in 1 country.
Funding
Abstract
Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. To address this, we applied and benchmarked multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form clusters highly corresponding to transcriptomic and morphological cell types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. The aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.
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