ClusterMap: multi-scale clustering analysis of spatial gene expression

Abstract

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

“…Considering the expert annotated structures as the gold standard, STAGATE achieved the best clustering accuracy (ARI = 0.544) compared to other five methods, while SpaGCN ranked the second (ARI = 0.484). Moreover, in light of the connections of the spatial domain identification and the single-cell segmentation (e.g., ClusterMap 37 and Baysor 38 ) designed for image-based ST data, we expect that the idea of STAGATE can be extended to single-cell segmentation task for the ongoing subcellular resolution technologies (e.g., Stereo-seq and PIXEL-seq) in the near future. We also expect to improve its applicability through the usage on datasets generated by new technologies.…”

SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data

“…Inaccurate cell segmentation can lead to misassignment of mRNA molecules to cells, leading to errors in downstream analysis such as misclassifying cell types. To overcome this issue, a number of computational tools have been developed to improve the assignment of mRNA molecules to cells ( Qian et al, 2020 ; Prabhakaran et al, 2021 ), incorporate cell typing as part of the segmentation process ( Littman et al, 2021 ), and perform cell-segmentation free analysis ( Petukhov et al, 2020 ; He et al, 2021 ; Park et al, 2021 ). While these tools improve cell typing, they all share the problem of being specialized tools that require access to Linux command line terminals, programming expertise, and high-performance hardware.…”

ReDisX: a Continuous Max Flow-based framework to redefine the diagnosis of diseases based on identified patterns of genomic signatures

“…Transforming the conventional diagnosis strategy for diseases is emerging for ensuring a healthier lifespan (Zhao et al, 2020) and for strengthening drug repurposing (Pushpakom et al, 2019). In the modern era, the practices for diagnosing diseases have been progressively oriented towards precision and personalized, relying on a molecular genetic basis, especially gene expression-based (Aure et al, 2017; He et al, 2021). Since the conventional diagnosis of diseases often remains insufficient in explaining heterogeneity within a disease and the homogeneity between multiple diseases (Humby et al, 2019; Khera & Kathiresan, 2017).…”