AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.

ABSTRACT:

Motivation

Dropout events bring challenges in analyzing single-cell RNA sequencing data as they introduce noise and distort the true distributions of gene expression profiles. Recent studies focus on estimating dropout probability and imputing dropout events by leveraging information from similar cells or genes. However, the number of dropout events differs in different cells, due to the complex factors, such as different sequencing protocols, cell types, and batch effects. The dropout event differences are not fully considered in assessing the similarities between cells and genes, which compromises the reliability of downstream analysis.

Results

This work proposes a hybrid Generative Adversarial Network with dropouts identification to impute single-cell RNA sequencing data,

SUBMITTER: Zhu X 

PROVIDER: S-EPMC10877090 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature