Analysis of community connectivity in spatial transcriptomics data

Abstract

AbstractThe 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 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 concept of analysis of community connectivity (ACC), which entails not only labeling distinct cell communities within a tissue sample, but understanding the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for integration of spatial and gene expression information to achieve ACC. We use BANYAN to implement ACC in invasive ductal carcinoma, and uncover distinct community structure relevant to the interaction of cell types within the tumor microenvironment. Next, we show how ACC can help clarify ambiguous annotations in a human white adipose tissue sample. Finally, we demonstrate BANYAN’s ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data.AvailabilityAn R package banyan is available at https://github.com/carter-allen/banyan.Contactchung.911@osu.eduSupplementary informationSupplementary data are available online.Author SummaryThe proliferation of spatial transcriptomics technologies have prompted the development of numerous statistical models for characterizing the makeup of a tissue sample in terms of distinct cell sub-populations. However, existing methods regard inferred sub-populations as static entities and do not offer any ability to discover the relative similarity of cells within and between communities, thereby obfuscating the true interactive nature of cells in a tissue sample. We develop BANYAN: a statistical model for implementing analysis of community connectivity (ACC), i.e., the process of inferring the similarity of cells within and between cell sub-populations. We demonstrate the utility of ACC through the analysis of a publicly available breast cancer data set, which revealed distinct community structure between tumor suppressive and invasive cancer cell sub-populations. We then showed how ACC may help elucidate ambiguous sub-population annotations in a publicly available human white adipose tissue data set. Finally, we implement a simulation study to validate BANYAN’s ability to recover true community connectivity structure in HST data.