The PhysMAP framework, which weights multiple electrophysiological modalities simultaneously, better aligned with known transcriptomically-defined cell types than any single modality alone.
PhysMAP is a novel multimodal computational tool that improves the identification and visualization of electrophysiological cell types in vivo compared to single-modality approaches.
Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a framework adapted from multiomics data analysis that weights multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that this alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can transfer labels to unannotated recordings and confirm that inferred cell types exhibit properties consistent with ground truth. Crucially, we show that PhysMAP can also be used to iteratively detect batch effects which confound classification. Together, these results establish PhysMAP as a tool for studying multiple cell types simultaneously and gaining insight into neural circuit dynamics.
Lee et al. (Wed,) conducted a other in Electrophysiological cell type identification. PhysMAP framework vs. Single modality alone was evaluated on Alignment with known transcriptomically-defined cell types. The PhysMAP framework, which weights multiple electrophysiological modalities simultaneously, better aligned with known transcriptomically-defined cell types than any single modality alone.