Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) provides extensive opportunities to explore cellular heterogeneity but is often limited by substantial technical noise and variability. The prevalence of zero counts, arising from both biological variation and technical dropout events, poses significant challenges for downstream analyses. Existing imputation methods face inherent trade-offs: statistical approaches maintain interpretability but exhibit limited capacity for capturing complex, non-linear gene expression relationships, whereas deep learning methods demonstrate superior flexibility but are prone to overfitting and lack mechanistic interpretability, particularly in settings with limited sample sizes.

Methods

We present ZILLNB (Zero-Inflated Latent factors Learning-

SUBMITTER: Luo Q 

PROVIDER: S-EPMC12629073 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature