![]() ![]() Transcriptomic activities such as gene expression for cellular characteristics and behaviors are fundamentally governed by gene regulatory networks (GRNs) 7. However, understanding the molecular mechanisms underlying multi-modalities that typically involve multiple genes is still challenging. Also, predictability from one modality to another has been found, such as predicting electrophysiological features from gene expression 6. Besides, recent studies have also identified several cell types from different modalities that share many cells (e.g., me-type), suggesting the linkages across modalities in these cells 2, 5. For instance, previous correlation-based analyses found individual genes whose expression levels linearly correlate with electrophysiological features in excitatory and inhibitory neurons 3, 4. Those cell types build a foundation for uncovering cellular functions, structures, and behaviors at different scales. The same type’s cells share similar characteristics: t-type by transcriptomics and e-type by electrophysiology. Further computational analyses have clustered cells into many cell types for each modality. For example, recent Patch-seq techniques enable measuring multiple characteristics of individual neuronal cells, including transcriptomics, morphology, and electrophysiology in the complex brains, also known as single-cell multimodal data 2. Recent single-cell technologies have generated great excitement and interest in studying functional genomics at cellular resolution 1. 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 aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. 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. We found that nonlinear manifold learning outperforms other methods. 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. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology.
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