Single-cell RNA-seq data augmentation using generative Fourier transformer.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for dissecting cellular complexity and heterogeneity. However, its full potential to achieve statistically reliable conclusions is often constrained by the limited number of cells profiled, particularly in studies of rare diseases, specialized tissues, and uncommon cell types. Deep learning-based generative models (GMs) designed to address data scarcity often face similar limitations due to their reliance on pre-training or fine-tuning, inadvertently perpetuating a cycle of data inadequacy. To overcome this obstacle, we introduce scGFT (single-cell Generative Fourier Transformer), a train-free, cell-centric GM adept at synthesizing single cells that exhibit natural gene expression profiles present within authentic datasets. Us

SUBMITTER: Nouri N 

PROVIDER: S-EPMC11754799 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature