Gala Rohan, Budzillo Agata, Baftizadeh Fahimeh, Miller Jeremy, Gouwens Nathan, Arkhipov Anton, Murphy Gabe, Tasic Bosiljka, Zeng Hongkui, Hawrylycz Michael, Sümbül Uygar
Allen Institute, Seattle, WA, USA.
Nat Comput Sci. 2021 Feb;1(2):120-127. doi: 10.1038/s43588-021-00030-1. Epub 2021 Feb 22.
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although 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.
在不同实验模式下一致地识别神经元是神经科学中的一个关键问题。尽管已经有了在同一组单个神经元中进行多模态测量的方法，但解析不同模式之间的复杂关系以揭示神经元身份仍是一个日益严峻的挑战。在这里，我们提出了一个优化框架，用于学习多模态数据的协调表示，并将其应用于一个描绘小鼠皮质中间神经元的大型多模态数据集。我们的方法揭示了转录组学和电生理特征之间的强一致性，实现了准确的跨模态数据预测，并识别了跨模式一致的细胞类型。
