Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network
Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network
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DOI：
10.1093/bioinformatics/btab812
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发表时间：
2021-12
期刊：
影响因子：
5.8
通讯作者：
Tianci Song;Kathleen K Markham;Zhuliu Li;K. Muller;Kathleen;Greenham;R. Kuang
中科院分区：
文献类型：
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作者：
Tianci Song;Kathleen K Markham;Zhuliu Li;K. Muller;Kathleen;Greenham;R. Kuang
动机空间分辨基因表达的聚类分析是揭示基因在潜在形态背景下的功能作用的必要分析。然而，传统的聚类分析不考虑组织中的基因表达共定位，用于检测空间表达模式或基因之间的功能关系，用于在空间背景下的生物学解释。在本文中，我们提出了一个卷积神经网络（CNN）正则化的蛋白质-蛋白质相互作用（PPI）网络的图形聚类空间分辨基因表达。该方法提高了空间模式的一致性，并通过利用卷积的空间定位和图形-拉普拉斯正则化的基因功能关系，在空间背景下提供基因簇的生物学解释。结果在实验中，我们测试了对来自10 x Genomics和spatialLIBD的不同组织切片的22个Visium空间转录组数据集中的转录组中的空间可变基因或所有表达基因进行聚类。结果表明，PPI正则化CNN不断检测具有连贯空间模式的基因簇，并通过基因功能显着富集，具有最先进的性能。对小鼠肾脏组织和人类乳腺癌组织的其他案例研究表明，PPI正则化CNN还检测了空间共表达的基因，以定义组织中相应的形态背景，具有宝贵的见解。源代码可在https://github.com/kuanglab/CNN-PReg上获得。
MOTIVATION Clustering spatial-resolved gene expressions 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 paper, we present a Convolutional Neural Network (CNN) regularized by the graph of Protein-Protein Interaction (PPI) network to cluster spatially-resolved gene expressions. 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. RESULTS In the experiments, 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 10x 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. AVAILABILITY Source code is available at https://github.com/kuanglab/CNN-PReg.