背景:空间转录组学技术充分利用了空间位置信息,组织形态特征,和转录谱。整合这些数据可以极大地促进我们对形态学背景下细胞生物学的理解。
方法:我们通过结合图神经网络开发了一种创新的空间聚类方法,称为STGNNks,去噪自动编码器,和k-sum聚类。首先,对空间分辨转录组学数据进行预处理,构造混合邻接矩阵。接下来,通过基于深图信息的图卷积网络,基因表达式和空间上下文被集成以学习点嵌入特征。第三,通过基于零膨胀负二项式(ZINB)的去噪自动编码器,将学习的特征映射到低维空间。第四,结合k均值聚类和比率割聚类算法,开发了一种k和聚类算法来识别空间域。最后,它实现了空间轨迹推断,空间可变基因识别,和差异表达基因检测基于伪时空方法在六个10x基因组铯数据集。
结果:我们将我们提出的STGNNks方法与其他五种空间聚类方法进行了比较,CCST,Seurat,stLearn,Scanpy和SEDR。第一次,机器学习领域的四个内部指标,也就是说,轮廓系数,戴维斯-博尔丁指数,Caliniski-Harabasz指数,和S_Dbw索引,用于使用CCST测量STGNNks的聚类性能,Seurat,stLearn,Scanpy和SEDR在五个没有标签的空间转录组学数据集上(即,成年小鼠大脑(FFPE),成年小鼠肾脏(FFPE),人类乳腺癌(A组2),人类乳腺癌(FFPE),和人类淋巴结)。并应用两个外部指标,包括调整后的Rand指数(ARI)和归一化互信息(NMI),以真实标签评估上述六种方法对人类乳腺癌的性能(第1部分)。比较实验表明,STGNNks获得了最小的Davies-Bouldin和S_Dbw值和最大的剪影系数,Caliniski-Harabasz,ARI和NMI,显著优于上述五种空间转录组学分析算法。此外,我们在上述5个未标记的数据集上检测到每个簇的前6个空间可变基因和前5个差异表达基因。具有分层布局的伪时空树图显示了人类乳腺癌(A部分1)在从三个浸润性导管癌区域分支到多个导管癌原位癌亚组的三个分支中的进展。
结论:我们预计STGNNks可以有效改善空间转录组学数据分析,并进一步促进相关疾病的诊断和治疗。这些代码可在https://github.com/plhhnu/STGNNks上公开获得。
BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.
METHODS: We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots\' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets.
RESULTS: We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters.
CONCLUSIONS: We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.