ScVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is crucial for elucidating gene expression dynamics and cellular heterogeneity at the individual cell level, thereby advancing our understanding of transcriptional regulation across distinct cell populations. However, a significant challenge in scRNA-seq data analysis is the prevalence of dropout events, which complicate downstream analyses. Most existing imputation tools either rely solely on linear assumptions or overlook the non-linear regulatory relationships embedded in the data. To address this issue, we propose single-cell variational graph autoencoder and matrix factorization (scVGAMF), a novel imputation method that integrates both linear and non-linear features. Specifically, scVGAMF first identifies highly variable genes and partitions them

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC12554638 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature