论文标题
稀疏的瓶颈神经网络，用于探索性非线性可视化斑块ze数据
Sparse bottleneck neural networks for exploratory non-linear visualization of Patch-seq data
论文作者
论文摘要
Patch-seq是一种最近开发的实验技术，使神经科学家可以从同一神经元获得转录组和电生理信息。但是，有效地分析和可视化此类配对的多元数据以提取生物学上有意义的解释仍然是一个挑战。在这里，我们使用有或没有二维瓶颈的稀疏深神经网络，使用组Lasso惩罚来预测转录组的电生理特征，从而产生简洁而生物学上可解释的二维可视化。在两个大型示例数据集中，这种可视化揭示了无生物学先验知识的已知神经类别及其标记基因。我们还证明我们的方法适用于其他类型的多模式数据，例如Cite-Seq提供的配对转录组和蛋白质组学测量。
Patch-seq, a recently developed experimental technique, allows neuroscientists to obtain transcriptomic and electrophysiological information from the same neurons. Efficiently analyzing and visualizing such paired multivariate data in order to extract biologically meaningful interpretations has, however, remained a challenge. Here, we use sparse deep neural networks with and without a two-dimensional bottleneck to predict electrophysiological features from the transcriptomic ones using a group lasso penalty, yielding concise and biologically interpretable two-dimensional visualizations. In two large example data sets, this visualization reveals known neural classes and their marker genes without biological prior knowledge. We also demonstrate that our method is applicable to other kinds of multimodal data, such as paired transcriptomic and proteomic measurements provided by CITE-seq.
