CAJAL enables analysis and integration of single-cell morphological data using metric geometry.

ABSTRACT: High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic da

SUBMITTER: Govek KW 

PROVIDER: S-EPMC10282047 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature