CoCo-ST: Comparing and Contrasting Spatial Transcriptomics data sets using graph contrastive learning.

ABSTRACT: Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a t

SUBMITTER: Aminu M 

PROVIDER: S-EPMC11142361 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature