Improved gene regulatory network inference from single cell data with dropout augmentation.

ABSTRACT: A major challenge in working with single-cell RNA sequencing data is the prevalence of "dropout," when some transcripts' expression values are erroneously not captured. Addressing this issue, which produces zero-inflated count data, is crucial for many downstream data analyses including the inference of gene regulatory networks (GRNs). In this paper, we introduce two novel contributions. First, we propose Dropout Augmentation (DA), a simple but effective model regularization method to improve resilience to zero inflation in single-cell data by augmenting the data with synthetic dropout events. DA offers a new perspective to solve the "dropout" problem beyond imputation. Second, we present DAZZLE, a stabilized and robust version of the autoencoder-based structure equation model for GRN infe

SUBMITTER: Zhu H 

PROVIDER: S-EPMC12574904 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature