ScFTAT: a novel cell annotation method integrating FFT and transformer.

ABSTRACT:

Background

Advancements in high-throughput sequencing and deep learning have boosted single-cell RNA studies. However, current methods for annotating single-cell data face challenges due to high data sparsity and tedious manual annotation on large-scale data.

Results

Thus, we proposed a novel annotation model integrating FFT (Fast Fourier Transform) and an enhanced Transformer, named scFTAT. Initially, it reduces data sparsity using LDA (Linear Discriminant Analysis). Subsequently, automatic cell annotation is achieved through a proposed module integrating FFT and an enhanced Transformer. Moreover, the model is fine-tuned to improve training performance by effectively incorporating such techniques as kernel approximation, position encoding enhancement, and attention enhancem

SUBMITTER: Tang B 

PROVIDER: S-EPMC11853718 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature