A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

Abstract

Recent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

Cellular age explains variation in age-related cell-to-cell transcriptome variability

“…The second method, GCL, randomly splits genes into two sets and quantifies their mutual dependence via a distance correlation metric between cell samples evaluated on the two gene sets. This metric was shown to capture both linear and nonlinear gene correlations and is robust to noise in single-cell data ( Vaknin et al 2021 ). Highly correlated gene expression patterns among cells give a high GCL, whereas a low GCL value indicates low levels of similarity in gene expression profiles.…”

Cell age drives asynchronous transcriptome aging

“…The second method, GCL, randomly splits genes into two sets and quantifies their mutual dependence via a distance correlation metric between cell samples evaluated on the two gene sets. This metric was shown to capture both linear and non-linear gene correlations and is robust to noise in single-cell data (Vaknin et al 2021). Highly correlated gene expression patterns among cells give a high GCL, while a low GCL value indicates low levels of similarity in gene expression profiles.…”

Global coordination level in single-cell transcriptomic data

“…By avoiding the network reconstruction, the GCL also has the advantage that it does not assume pair-wise interactions or a specific form of relation (e.g., linear relations), and it does not assume the same type of interaction for all the pairs of interacting genes. The GCL was also found useful in distinguishing between cell-to-cell variability induced by biological process and variability induced by technical noise in simulations of cellular networks 16 .…”