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...

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Autores principales:	Dana Vaknin, Guy Amit, Amir Bashan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f1c0ec73b70441e4a0b383a38b2dc5a3
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spelling	oai:doaj.org-article:f1c0ec73b70441e4a0b383a38b2dc5a32021-12-02T15:00:59ZA top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability10.1038/s41598-021-90353-w2045-2322https://doaj.org/article/f1c0ec73b70441e4a0b383a38b2dc5a32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90353-whttps://doaj.org/toc/2045-2322Abstract 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.Dana VakninGuy AmitAmir BashanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dana Vaknin Guy Amit Amir Bashan A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
description	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.
format	article
author	Dana Vaknin Guy Amit Amir Bashan
author_facet	Dana Vaknin Guy Amit Amir Bashan
author_sort	Dana Vaknin
title	A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
title_short	A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
title_full	A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
title_fullStr	A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
title_full_unstemmed	A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
title_sort	top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f1c0ec73b70441e4a0b383a38b2dc5a3
work_keys_str_mv	AT danavaknin atopdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability AT guyamit atopdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability AT amirbashan atopdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability AT danavaknin topdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability AT guyamit topdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability AT amirbashan topdownmeasureofgenetogenecoordinationforanalyzingcelltocellvariability
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A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

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