Open AccessRevealing lineage-related signals in single-cell gene expression using random matrix theory
Author(s) -
Mor Nitzan,
Michael P. Brenner
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.1913931118
Subject(s) - biology , computational biology , covariance , lineage (genetic) , covariance matrix , signature (topology) , biological system , gene expression , gene , single cell analysis , evolutionary biology , cell , genetics , computer science , algorithm , mathematics , statistics , geometry
Significance Single-cell technologies are rapidly advancing, allowing us to gauge the heterogeneity and structure of cellular communities, tissues, and full organisms. The correlations between genes and between cells within such systems can reveal patterns of regulatory interactions, physical structure, and temporal progression of cells along biological processes. However, it is generally a challenge to identify and tease apart these mixed signals within the noisy, high-dimensional single-cell data. Here, we show it is possible to detect a signature for lineage in the covariance spectrum of single-cell data, predict how it will change with developmental time, and predict how it can be extended to examine the spatial structure of a tissue.