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

TL;DR

This paper introduces a novel method combining a density-based minimum spanning tree and a tree-biased autoencoder to visualize hierarchical structures in high-dimensional scRNA-seq data, facilitating biological interpretation.

Contribution

The authors present a new approach for extracting and visualizing tree structures in scRNA-seq data using a density tree and a specialized autoencoder, improving interpretability of cellular differentiation.

Findings

The method accurately captures biological hierarchy in data.

It outperforms other dimension reduction techniques.

The approach is validated on real and synthetic datasets.

Abstract

Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data is 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 minimum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data.Availability: Our implementation relying on PyTorch and Higra is available at https://github.com/hci-unihd/DTAE.