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Consistent cross-modal identification of cortical neurons with coupled autoencoders

By Rohan Gala, Agata Budzillo, Fahimeh Baftizadeh, Jeremy Miller, Nathan W. Gouwens, Anton Arkhipov, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Michael Hawrylycz, Uygar Sümbül

Posted 02 Jul 2020
bioRxiv DOI: 10.1101/2020.06.30.181065

Consistent identification of neurons and neuronal cell types across different observation modalities is an important problem in neuroscience. Here, we present an optimization framework to learn coordinated representations of multimodal data, and apply it to a large Patch-seq dataset of mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological profiles of neurons, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities. ### Competing Interest Statement The authors have declared no competing interest.

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