Spatial transcriptome (ST) technology provides both the spatial location and transcriptional profile of spots, as well as tissue images. ST data can be utilized to construct gene regulatory networks, which can help identify gene modules that facilitate the understanding of biological processes such as cell communication. Correlation measurement is the core basis for constructing a gene regulatory network. However, due to the high noise and sparsity in ST data, common correlation measurement methods such as the Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SPCC) are not suitable. In this work, a new gene correlation measurement method called STgcor is proposed. STgcor defines vertexes as spots in a two-dimensional coordinate plane consisting of axes X and Y from the gene pair (X and Y). The joint probability density of Gaussian distribution of the gene pair (X and Y) is calculated to identify and eliminate outliers. To overcome sparsity, the degree, trend, and location of the distribution of vertexes are used to measure the correlation between gene pairs (X, Y). To validate the performance of the STgcor method, it is compared with the PCC and SPCC in a weighted coexpression network analysis method using two ST datasets of breast cancer and prostate cancer. The gene modules identified by these methods are then compared and analyzed. The results show that the STgcor method detects some special gene modules and cancer-related pathways that cannot be detected by the other two methods.

中文翻译：

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基于分区和分布的空间转录组数据基因相关性测量方法。

空间转录组 (ST) 技术提供斑点的空间位置和转录概况以及组织图像。ST 数据可用于构建基因调控网络，这有助于识别基因模块，从而促进对细胞通讯等生物过程的理解。相关性测量是构建基因调控网络的核心基础。然而，由于ST数据的高噪声和稀疏性，常用的相关性测量方法，如皮尔逊相关系数（PCC）和斯皮尔曼相关系数（SPCC）并不适用。在这项工作中，提出了一种新的基因相关性测量方法，称为 STgcor。STgcor 将顶点定义为二维坐标平面中的点，该二维坐标平面由基因对（X 和 Y）中的 X 轴和 Y 轴组成。计算基因对（X和Y）的高斯分布的联合概率密度以识别和消除异常值。为了克服稀疏性，利用顶点分布的程度、趋势和位置来衡量基因对（X，Y）之间的相关性。为了验证 STgcor 方法的性能，使用乳腺癌和前列腺癌的两个 ST 数据集，在加权共表达网络分析方法中将其与 PCC 和 SPCC 进行比较。然后对通过这些方法鉴定的基因模块进行比较和分析。结果表明，STgcor方法检测到了其他两种方法无法检测到的一些特殊基因模块和癌症相关通路。