004 - Manifold learning analysis suggests novel strategies to align single-cell multi-modal data of neuronal electrophysiology and transcriptomics

We are focusing on a type of data set that emerge recently by the advance of single-cell technologies - multi-modal data, which are usually refer to the data set with multiple feature sets pointing to the same individual. With the development of recent single-cell technologies, it has generated a great deal of excitement and interest in studying functional genomics at cellular resolution. 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 multi-modal data. More detailly, Patch-seq experiments profile the electrophysiological properties and transcriptome of the same individual neurons, with the goal of identifying the underlying relationships between gene expression and neuronal function. Additionally, recorded neurons can be backfilled with appropriate dies to evaluate cell morphology.

We used nonlinear manifold alignment to align multiple features of single-cell data, which are cell transcriptomics and electrophysiological features. It provided a manifold workflow that applies statistical and machine learning methods to reduce the high dimensional multi-modal data into a 3D manifold, followed by clustering by Gaussian Mixture Kernel, electrophysiological feature prediction, functional enrichment, and gene regulatory network analyses, we showed the underlying relationships between gene expression and neuronal function. The good performance of nonlinear manifold alignment, compared to other methods on single-cell multiple data, suggests that our method is interpretable, able to show the 3D trajectory that have not been shown by other studies, and have a strong prediction power between identified features, thus should be useful and appliable to all similar data types in different industries or application areas as well.

Manifold Alignment

Single-cell Multi-Model Data

Functional Genomics

Gene Expression

Electrophysiology

Gene Regulation 

Mid-Level

Community

International Conference on Health Policy Statistics 2023