Manifold learning analysis reveals the functional genomics at the cell-type level for neuronal electrophysiology in the mouse brain

Recent single-cell multi-modal data reveal different characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, our understanding of functional genomics and gene regulation leading to the cellular characteristics remains elusive. To address this, we used emerging manifold learning to align gene expression and electrophysiological data of single neuronal cells in the mouse brain. After manifold alignment, the cell clusters highly correspond to transcriptomic and morphological cell-types, suggesting a strong nonlinear linkage between gene expression and electrophysiology at the cell-type level. Additional functional enrichment and gene regulatory network analyses revealed potential novel molecular mechanistic insights from genes to electrophysiology at cellular resolution.