Otto Dominik J, Arriaga-Gomez Erica, Thieme Elana, Yang Ruijin, Lee Stanley C, Setty Manu
Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA.
Computational Biology Program, Public Health Sciences Division, Seattle WA.
bioRxiv. 2025 Jun 7:2025.06.03.657769. doi: 10.1101/2025.06.03.657769.
Kompot is a statistical framework for holistic comparison of multi-condition single-cell datasets, supporting both differential abundance and differential expression. Differential abundance captures changes in how cells populate the phenotypic manifold across conditions, while differential expression identifies condition-specific changes in gene regulation that may be localized to particular regions of that manifold. Kompot models the distribution of cells and gene expression as continuous functions over a low-dimensional representation of cell states, enabling single-cell resolution inference with calibrated uncertainty estimates. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. By capturing both global and cell-state-specific effects of perturbation, Kompot reveals how aging reshapes cellular identity and regulatory programs across the hematopoietic landscape. This framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes.
Kompot是一个用于多条件单细胞数据集整体比较的统计框架，支持差异丰度和差异表达分析。差异丰度反映了不同条件下细胞在表型流形上分布的变化，而差异表达则识别出基因调控中特定条件下的变化，这些变化可能局限于该流形的特定区域。Kompot将细胞分布和基因表达建模为细胞状态低维表示上的连续函数，从而能够进行具有校准不确定性估计的单细胞分辨率推断。将Kompot应用于衰老的小鼠骨髓，我们识别出造血干细胞和成熟细胞状态的连续变化、单核细胞转录重塑独立于组成变化，以及不同细胞类型中氧化应激反应基因的不同调控。通过捕捉扰动的全局和细胞状态特异性效应，Kompot揭示了衰老如何重塑造血系统中的细胞身份和调控程序。该框架广泛适用于剖析复杂单细胞景观中特定条件下的效应。
