Generative pretraining from large-scale transcriptomes for single-cell deciphering.

ABSTRACT: Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretraining from transcriptomes (tGPT) for learning feature representation of transcriptomes. tGPT is conceptually simple in that it autoregressive models the ranking of a gene in the context of its preceding neighbors. We developed tGPT with 22.3 million single-cell transcriptomes and used four single-cell datasets to evalutate its performance on single-cell analysis tasks. In addition, we examine its applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by <

SUBMITTER: Shen H 

PROVIDER: S-EPMC10176267 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature