Structure-preserving visualization for single-cell RNA-Seq profiles using deep manifold transformation with batch-correction.

ABSTRACT: Dimensionality reduction and visualization play an important role in biological data analysis, such as data interpretation of single-cell RNA sequences (scRNA-seq). It is desired to have a visualization method that can not only be applicable to various application scenarios, including cell clustering and trajectory inference, but also satisfy a variety of technical requirements, especially the ability to preserve inherent structure of data and handle with batch effects. However, no existing methods can accommodate these requirements in a unified framework. In this paper, we propose a general visualization method, deep visualization (DV), that possesses the ability to preserve inherent structure of data and handle batch effects and is applicable to a variety of datasets from different appli

SUBMITTER: Xu Y 

PROVIDER: S-EPMC10073100 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature