Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.

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

SUBMITTER: Huang J 

PROVIDER: S-EPMC8604989 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature