A coupled autoencoder approach for multi-modal analysis of cell types
This work addresses the challenge of integrating multi-modal data in neuroscience to improve cell type classification, but it is incremental as it builds on existing autoencoder methods for a specific domain problem.
The authors tackled the problem of inconsistent cell type definitions across different data modalities by developing a coupled autoencoder framework for cross-modal alignment, applying it to a Patch-seq dataset to study representation consistency and cross-modal prediction, and demonstrating its ability to identify cell types from single-modality data.
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality.