Department of Biostatistics, University of Florida, Gainesville, FL 32603, USA.
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.
Genes (Basel). 2023 Jun 28;14(7):1368. doi: 10.3390/genes14071368.
Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell-cell communication and provide a robust and straightforward result.
细胞通过生化信号进行通讯是所有生物活动的基础。研究细胞间信号扩散 across cell types 可以帮助我们进一步了解生物学机制。近年来，随着单细胞测序技术的成熟，这已经成为一个重要的研究课题。然而，细胞信号活动受到空间限制，单细胞数据无法为每个细胞提供空间信息。这个问题可能会导致高假发现率，因此需要使用空间分辨转录组学数据。另一方面，据我们所知，大多数现有方法侧重于提供特定的测量来估计细胞间通讯，而不是依赖于统计模型。不可否认的是，描述性统计数据简单直接，但合适的统计模型可以提供更准确可靠的推断。在这种情况下，我们提出了一种广义线性回归模型，从空间分辨转录组学数据中推断细胞通讯，特别是基于点的 data。我们的 BAyesian Tweedie modeling of COMmunications (BATCOM) 方法考虑到细胞类型的相应距离，估计细胞类型之间的通讯分数。由于回归模型的性质，BATCOM 自然提供了细胞类型之间的通讯方向以及其他方法无法提供的配体和受体的相互作用。我们进行了模拟研究来评估不同场景下的性能。我们还在实际数据应用中使用 BATCOM 并将其与其他现有算法进行了比较。总之，我们的创新模型可以填补细胞间通讯推断中的空白，并提供稳健且直接的结果。
