A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination
复制标题
DOI：
10.1007/s12021-020-09461-z
复制
发表时间：
2020-05-04
期刊：
影响因子：
3
通讯作者：
Berens, Philipp
中科院分区：
文献类型：
--
作者：
Laturnus, Sophie;Kobak, Dmitry;Berens, Philipp
神经元形态的定量分析通常从选择特定的特征表示开始，以便使个体形态符合标准统计工具和机器学习算法。在文献中已经提出了许多不同的特征表示，从密度图到相交剖面，但它们从未被并排比较。在这里，我们对各种表示进行了系统的比较，测量它们能够捕获已知形态细胞类型之间的差异的程度。对于我们的基准测试工作，我们使用了几个由小鼠视网膜双极细胞和皮质抑制神经元组成的策划数据集。我们发现，表现最好的特征表示是二维密度图，二维持久性图像和形态统计，即使神经元只被部分追踪，它们也能继续表现良好。将这些特征表示组合在一起导致进一步的性能提高，这表明它们捕获了非冗余信息。相同的表示在无监督设置中表现良好，这意味着它们可以适用于降维或聚类。
Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side. Here we performed a systematic comparison of various representations, measuring how well they were able to capture the difference between known morphological cell types. For our benchmarking effort, we used several curated data sets consisting of mouse retinal bipolar cells and cortical inhibitory neurons. We found that the best performing feature representations were two-dimensional density maps, two-dimensional persistence images and morphometric statistics, which continued to perform well even when neurons were only partially traced. Combining these feature representations together led to further performance increases suggesting that they captured non-redundant information. The same representations performed well in an unsupervised setting, implying that they can be suitable for dimensionality reduction or clustering.