Analysis of community connectivity in spatial transcriptomics data

Abstract

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 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. An R package banyan is available at https://github.com/carter-allen/banyan.

LRT: T Cell Trajectory Inference by Integrative Analysis of Single-Cell TCR-seq and RNA-seq data

“…Let’s denote n as the total number of cells. To apply SBM in our case, we convert the DTW distance matrix to an adjacency matrix of a k-nearest neighbor graph, denoted as A , where we set by adopting the widely used heuristics [15, 16]. Given A = [ A ij ] ∈ ℝ n × n denoting the binary adjacency matrix of a random graph, with elements A ij indicating the presence or absence of an undirected edge between nodes i and j , we assume that the absence or presence of an edge between each pair of nodes i and j follows a Bernoulli distribution, i.e., …”

“…Finally, the DTW distance is defined as the cost associated with the optimal warping path, i.e., Based on this DTW distance matrix, LRT applies an SBM[14] to identify trajectory clusters (i.e., community detection). SBM is a generative model for random graphs, which is widely employed for recovering (latent) community structures in network/graph data[15]. Let's denote 𝑛 as the total number of cells.…”