ZiPo: A Deep Neural Network to De-Noise Single-Cell RNA Sequencing Data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq), a powerful technique for investigating the transcriptome of individual cells, enables the discovery of heterogeneous cell populations, rare cell types, and transcriptional dynamics in separate cells. Yet, scRNA-seq data analysis is limited by the problem of measurement dropouts, i.e., genes displaying zero expression levels. We introduce ZiPo, a deep artificial neural network for rate estimation and library size prediction in scRNA-seq data that incorporates adjustable zero inflation in the distribution to capture dropouts. ZiPo builds upon established concepts, including using deep autoencoders and adopting the Poisson and negative binomial distributions, by taking advantage of novel strategies, including library size prediction and residual connect

SUBMITTER: Sharifitabar M 

PROVIDER: S-EPMC12910556 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature