Cai Jiazhang, Wu Shushan, Cheng Huimin, Zhong Wenxuan, Yuan Guo-Cheng, Ma Ping
Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, USA.
Department of Biostatistics, Boston University, 801 Massachusetts Avenue Crosstown Center, Boston, MA 02118, USA.
STAR Protoc. 2025 Mar 21;6(1):103608. doi: 10.1016/j.xpro.2025.103608. Epub 2025 Jan 28.
Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix. We then detail procedures for using the similarity matrix to identify spatial domains and obtain new embeddings. For complete details on the use and execution of this protocol, please refer to Cai et al..
空间转录组学通过将基因表达数据映射到精确的组织位置，增强了我们对细胞组织的理解。在这里，我们提出了一种用于空间转录组学的加权集成方法（WEST）的方案，该方法使用集成技术来提高现有算法的稳健性和准确性。我们描述了数据预处理、从各个算法中获取嵌入以及将所有嵌入作为相似性矩阵进行集成的步骤。然后，我们详细说明了使用相似性矩阵识别空间域并获得新嵌入的过程。有关此方案的使用和执行的完整详细信息，请参考蔡等人的研究。
