Xie Juan, Jung Kyeong Joo, Allen Carter, Chang Yuzhou, Paul Subhadeep, Li Zihai, Ma Qin, Chung Dongjun
The Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH, United States.
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.
Front Appl Math Stat. 2024;10. doi: 10.3389/fams.2024.1403901. Epub 2024 Jul 11.
The advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for the characterization of community connectivity, i.e., the relative similarity of cells within and between found communities-an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment.
To address this gap, we introduce the analysis of community connectivity (ACC), which facilitates understanding of the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for the integration of spatial and gene expression information to achieve ACC.
We demonstrate BANYAN's ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data. Next, we use BANYAN to implement ACC across a wide range of real data scenarios, including 10× Visium data of melanoma brain metastases and invasive ductal carcinoma, and NanoString CosMx data of human-small-cell lung cancer, each of which reveals distinct cliques of interacting cell sub-populations. An R package banyan is available at https://github.com/dongjunchung/banyan.
高通量空间转录组学（HST）的出现使得对组织样本中空间上不同的细胞群落进行前所未有的表征成为可能。虽然存在多种用于检测HST数据中细胞群落的计算工具，但没有一种工具能够对群落连通性进行表征，即已发现群落内部和之间细胞的相对相似性——这是一项分析任务，可在肿瘤微环境等重要环境中阐明细胞动态。
为了填补这一空白，我们引入了群落连通性分析（ACC），它有助于理解群落内部和之间细胞的相对相似性。我们开发了一种名为BANYAN的贝叶斯多层网络模型，用于整合空间和基因表达信息以实现ACC。
我们通过基于真实小鼠脑矢状面HST数据的模拟研究，展示了BANYAN恢复群落连通性结构的能力。接下来，我们使用BANYAN在广泛的真实数据场景中实现ACC，包括黑色素瘤脑转移和浸润性导管癌的10× Visium数据，以及人小细胞肺癌的NanoString CosMx数据，每一个都揭示了相互作用的细胞亚群的不同团簇。可在https://github.com/dongjunchung/banyan获取R包banyan。
