Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Abstract

Spatially resolved transcriptomics (ST) provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications from ST data, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells, decomposed from ST data through a non-negative linear model and spatial mapping between single-cell RNA-sequencing and ST data. The performance of SpaTalk benchmarked on public single-cell ST datasets was superior to that of existing cell-cell communication inference methods. SpaTalk was then applied to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based ST data universally, providing new insights into spatial inter-cellular dynamics.

SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patterns

“…1F), despite the data being generated by a different model. In contrast, CellChat with 2 different parameter settings [4], Giotto [17], and SpaTalk [20] failed to control false positives, possibly due to the lack of effective modelling of spatial information.…”

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

“…We identified three recent or widely used tools that offer analysis at the multicellular resolution: stLearn (13), CellPhoneDB (10), and SpaTalk (14). We compared these tools with BulkSignalR using three human datasets: the previously used TNBC dataset (36), a HER2+ breast cancer dataset (41), and a dorsolateral prefrontal cortex dataset (42).…”

“…By simply adjusting few parameters, we found that BulkSignalR could be used for multicellular resolution spatial analyses successfully. We then compared the performance of BulkSignalR and of three tools for multicellular resolution analyses, CellPhoneDB (10), stLearn (13), and SpaTalk (14).…”

De novoanalysis of bulk RNA-seq data at spatially resolved single-cell resolution

“…Understanding the transcriptional heterogeneity within the tissue from the perspective of single-cell spatial resolution and full gene coverage is an essential direction for the development in the field of biological sciences 1, 56 . The spatial locations of cells may determine their identity and how they interplay with each other in the microenvironment 57, 58 . However, there are many technical challenges, and thus, such a technology has not been fully realized yet.…”