Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55414, USA.
Department of Plant and Microbial Biology, University of Minnesota Twin Cities, Minneapolis, MN 55414, USA.
Bioinformatics. 2022 Feb 7;38(5):1344-1352. doi: 10.1093/bioinformatics/btab812.
Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this article, we present a convolutional neural network (CNN) regularized by the graph of protein-protein interaction (PPI) network to cluster spatially resolved gene expression. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization.
In this study, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10× Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights.
Source code is available at https://github.com/kuanglab/CNN-PReg.
Supplementary data are available at Bioinformatics online.
通过功能作用对基因活动进行分析，是揭示潜在形态学背景下基因表达的关键。然而，传统的聚类分析没有考虑组织中基因表达的共定位，无法检测到空间表达模式或基因之间的功能关系，无法进行空间背景下的生物学解释。在本文中，我们提出了一种基于蛋白质-蛋白质相互作用网络（PPI）图的卷积神经网络（CNN）正则化方法，用于聚类空间分辨基因表达。该方法通过卷积进行空间定位，通过图拉普拉斯正则化进行基因功能关系，从而提高空间模式的一致性，并提供空间背景下基因聚类的生物学解释。
在这项研究中，我们测试了在 22 个来自 10×Genomics 和 spatialLIBD 的公开可用的不同组织切片的 Visium 空间转录组学数据集中对空间变化的基因或转录组中所有表达的基因进行聚类。结果表明，PPI 正则化 CNN 始终可以检测到具有一致空间模式的基因簇，并且通过基因功能显著富集，具有最先进的性能。对小鼠肾脏组织和人类乳腺癌组织的额外案例研究表明，PPI 正则化 CNN 还可以检测空间共表达基因，以定义组织中相应的形态学背景，并提供有价值的见解。
源代码可在 https://github.com/kuanglab/CNN-PReg 上获得。
补充数据可在生物信息学在线获得。
