Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA.
Northwestern Institute on Complex Systems, Evanston, IL 60208, USA.
Sci Adv. 2020 Mar 18;6(12):eaax7798. doi: 10.1126/sciadv.aax7798. eCollection 2020 Mar.
The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that-in contrast with existing methods-predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achieve these benefits by applying a -nearest-neighbors algorithm after projecting our data onto the eigenvectors of the correlation matrix inferred from many observations of gene expression or chromatin conformation. Our approach identifies variations in epigenotype that affect cell type, thereby supporting the cell-type attractor hypothesis and representing the first step toward model-independent control strategies in biological systems.
微观观察与宏观行为之间的关系是生物物理系统中的一个基本开放性问题。在这里，我们开发了一种统一的方法——与现有方法不同——即使在考虑到人类组织多样性的规模和可用数据的局限性的情况下，也可以从大分子数据中预测细胞类型。我们通过将数据投影到从许多基因表达或染色质构象观察中推断出的相关矩阵的特征向量上，然后应用最近邻算法来实现这些优势。我们的方法确定了影响细胞类型的表观遗传变异，从而支持细胞类型吸引子假说，并代表了在生物系统中实现无模型控制策略的第一步。
