All Times ET
Keywords: Unsupervised Learning, Manifold Learning, Gene expression, Electrophysiology, Multi-modal Data Alignment, Clustering
Recent single-cell multi-modal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multi-modal 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 Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form the 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. The codes and datasets for our analyses are available at https://github.com/daifengwanglab/scMNC.