Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Abstract Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are someti...

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Autores principales:	Thoma Itoh, Takashi Makino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9a3d106e512c4a54aad2f138fd9fd3f1
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spelling	oai:doaj.org-article:9a3d106e512c4a54aad2f138fd9fd3f12021-11-21T12:19:12ZCapturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast10.1038/s41598-021-01558-y2045-2322https://doaj.org/article/9a3d106e512c4a54aad2f138fd9fd3f12021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01558-yhttps://doaj.org/toc/2045-2322Abstract Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.Thoma ItohTakashi MakinoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Thoma Itoh Takashi Makino Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
description	Abstract Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.
format	article
author	Thoma Itoh Takashi Makino
author_facet	Thoma Itoh Takashi Makino
author_sort	Thoma Itoh
title	Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
title_short	Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
title_full	Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
title_fullStr	Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
title_full_unstemmed	Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
title_sort	capturing hidden regulation based on noise change of gene expression level from single cell rna-seq in yeast
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9a3d106e512c4a54aad2f138fd9fd3f1
work_keys_str_mv	AT thomaitoh capturinghiddenregulationbasedonnoisechangeofgeneexpressionlevelfromsinglecellrnaseqinyeast AT takashimakino capturinghiddenregulationbasedonnoisechangeofgeneexpressionlevelfromsinglecellrnaseqinyeast
_version_	1718419082046341120

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

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