Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.

ABSTRACT: Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis

SUBMITTER: Cheng A 

PROVIDER: S-EPMC9851325 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature