ScGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.

ABSTRACT: Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior

SUBMITTER: Song Q 

PROVIDER: S-EPMC8219725 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature