论文标题

时间序列中的拓扑数据分析:时间过滤和应用于单细胞基因组学

Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

论文作者

Lin, Baihan

论文摘要

在开发过程中,细胞细胞同居与其新兴动态之间没有常规关联,这阻碍了我们对细胞种群如何扩散,分化和竞争的理解,即细胞生态学。随着单细胞RNA测序(RNA-Seq)的最新进展,我们可以通过构造网络图来描述这种链接,从而表征细胞特异性转录程序的基因表达谱的相似性,并使用由eLgebraic topolage the Elgebraic topology信息进行系统地分析这些图。我们提出了单细胞拓扑简单分析(SCTSA)。将这种方法应用于不同发育阶段的细胞局部网络的单细胞基因表达谱,这表明了以前看不见的细胞生态拓扑。这些网络包含大量的单细胞剖面丛中的腔,这些腔体引导了更复杂的居住形式的出现。与无效模型相比,我们使用这些网络的拓扑简单架构可视化这些生态模式。我们的方法以38,731个细胞,25种细胞类型和12个时间步长的斑马鱼胚发生的单细胞RNA-seq数据进行了基准测试,我们的方法强调了胃分解是最关键的阶段,与发育生物学的共识一致。作为非线性,独立和无监督的框架,我们的方法也可以应用于追踪多规模的细胞谱系,识别关键阶段或创建伪时间系列。

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.

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