Relative stability of mRNA and protein severely limits inference of gene networks from single-cell mRNA measurements

Author:

Mahajan TarunORCID,Saint-Antoine Michael,Dar Roy D.ORCID,Singh AbhyudaiORCID

Abstract

AbstractInference of gene regulatory networks from single-cell expression data, such as single-cell RNA sequencing, is a popular problem in computational biology. Despite diverse methods spanning information theory, machine learning, and statistics, it is unsolved. This shortcoming can be attributed to measurement errors, lack of perturbation data, or difficulty in causal inference. Yet, it is not known if kinetic properties of gene expression also cause an issue. We show how the relative stability of mRNA and protein hampers inference. Available inference methods perform benchmarking on synthetic data lacking protein species, which is biologically incorrect. We use a simple model of gene expression, incorporating both mRNA and protein, to show that a more stable protein than mRNA can cause loss in correlation between the mRNA of a transcription factor and its target gene. This can also happen when mRNA and protein are on the same timescale. The relative difference in timescales affects true interactions more strongly than false positives, which may not be suppressed. Besides correlation, we find that information-theoretic nonlinear measures are also prone to this problem. Finally, we demonstrate these principles in real single-cell RNA sequencing data for over 1700 yeast genes.

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3