AllenInstitute / coupledAE-patchseq

Multimodal data alignment and cell type analysis with coupled autoencoders.
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autoencoders celltypes multimodal patchseq representation-learning

Consistent cross-modal identification of cortical neurons with coupled autoencoders

@article{gala2021consistent,
  title={Consistent cross-modal identification of cortical neurons with coupled autoencoders},
  author={Gala, Rohan and Budzillo, Agata and Baftizadeh, Fahimeh and Miller, Jeremy and Gouwens, Nathan and Arkhipov, Anton and Murphy, Gabe and Tasic, Bosiljka and Zeng, Hongkui and Hawrylycz, Michael and S{\"u}mb{\"u}l, Uygar},
  journal={Nature Computational Science},
  volume={1},
  number={2},
  pages={120--127},
  year={2021},
  publisher={Nature Publishing Group}
}

Abstract

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here, we present an optimization framework to learn coordinated representations of multimodal data, and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.

Data

Code

You can also play around with a minimal version of the coupled autoencoders code (see minimal folder in this repository) hosted on a cloud environment at CodeOcean.

See also:

A coupled autoencoder approach for multi-modal analysis of cell types, Gala R. et al, Advances in Neural Information Processing Systems 32, 9267--9276, 2019.

The main points covered by earlier work: