AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) technology has become an effective tool for high-throughout transcriptomic study, which circumvents the averaging artifacts corresponding to bulk RNA-seq technology, yielding new perspectives on the cellular diversity of potential superficially homogeneous populations. Although various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material, the technical noise and biological variation are inevitably introduced into experimental process, resulting in high dropout events, which greatly hinder the downstream analysis. Considering the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data, we propose a customized autoencoder base

SUBMITTER: Zhao S 

PROVIDER: S-EPMC9607720 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature