DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data

Abstract

Motivation: Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging.

Results: We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data.

Availability and implementation: DensityPath software is available at https://github.com/ucasdp/DensityPath.

Supplementary information: Supplementary data are available at Bioinformatics online.