Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data.

ABSTRACT: Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to liga

SUBMITTER: Zhu J 

PROVIDER: S-EPMC10541142 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature