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
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Publicado: Nature Portfolio 2021
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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
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