Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.

Results

Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree structure. We extract the tree structure by means of a density-based maximum spanning tree on a vector quantization of the data and show that it captures biological information well. We t

SUBMITTER: Garrido Q 

PROVIDER: S-EPMC9235514 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature