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Comprehensive Visualization of High-Dimensional Single-Cell Data With Diffusion-Based Manifold Approximation and Projection (dbMAP)
43 Pages Posted: 17 May 2020 Publication Status: Review Complete
More...Abstract
Ongoing advances in cell biology enable the molecular profiling of hundreds of thousands of single cells with an ever-growing sequencing depth and sample size. A generalized approach capable of visualizing this high-dimensional data in a comprehensive fashion remains a challenge in the field. Here, we present diffusion-based Manifold Approximation and Projection, a novel dimensionality reduction method tailored for the comprehensive visualization of single-cell data. Comparisons of dbMAP to other methods using publicly available single-cell data of human hematopoiesis, murine corticogenesis and whole-mouse embryogenesis show that dbMAP visualizations better recapitulate the known biology. Remarkably, dbMAP visually depicts the cell cycle underlying complex biological systems, and its unprecedented resolution intuitively provides novel biological insights when applied to previously analyzed data. We provide a generalized and computationally efficient approach to comprehensively visualize single-cell transcriptome that is particularly well-suited for the analysis of tissue and whole-organism data, and that further escalates the hypothesis-generating power of single-cell analyses.
Keywords: Single-cell RNA, analysis, graphical representation, high-dimension
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