KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001, Leuven, Belgium.
Aspect Analytics NV, C-mine 12, 3600, Genk, Belgium.
Anal Bioanal Chem. 2021 Apr;413(10):2803-2819. doi: 10.1007/s00216-021-03179-w. Epub 2021 Mar 1.
Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the relative isotope ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.
计算分析对于利用质谱成像（MSI）实验产生的丰富的空间分子信息至关重要。目前，由于深度空间模式提取的挑战，MSI 数据中可用的空间信息往往未被充分利用。深度学习的出现极大地促进了这种复杂的空间分析。在这项工作中，我们使用预训练的神经网络从 MSI 数据中的离子图像中提取高级特征，并测试这是否能改进下游数据分析。由此产生的离子图像的神经网络解释，称为神经离子图像，用于根据空间表达对离子图像进行聚类。我们评估了神经离子图像对两种离子图像聚类管道的影响，即 DBSCAN 聚类，结合基于 UMAP 的降维，以及 k-均值聚类。在这两种管道中，我们比较了来自两个不同 MSI 数据集的常规和神经离子图像。所有测试的管道都可以提取潜在的空间模式，但基于神经网络的管道提供了更好的离子图像分配，具有更细粒度的簇，并且分配给各个簇的空间结构更一致。此外，我们引入了相对同位素比度量来定量评估聚类质量。所得分数表明，在基于神经网络的管道中，同位素 m/z 值更经常聚类在一起，表明聚类结果得到了改善。神经离子图像的用途不仅限于聚类，还可以扩展到通用框架，将空间信息纳入任何针对 MSI 的机器学习管道，包括监督和无监督学习。
