Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
This provides a nonlinear, unsupervised framework for analyzing single-cell genomics data to trace cell lineage and identify critical stages, addressing a gap in linking cell cohabitation to emergent dynamics in developmental biology.
The paper tackled the problem of understanding cell ecology in development by analyzing single-cell RNA-seq data using topological data analysis, proposing scTSA to reveal unseen topological patterns in cellular networks, with results highlighting gastrulation as the most critical stage in zebrafish embryogenesis across 38,731 cells and 12 time steps.
The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq), we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types and 12 time steps, our approach highlights the gastrulation as the most critical stage, consistent with consensus in developmental biology. As a nonlinear, model-independent, and unsupervised framework, our approach can also be applied to tracing multi-scale cell lineage, identifying critical stages, or creating pseudo-time series.