Structural Equation Modeling of In silico Perturbations

Gene expression is controlled by multiple regulators and their interactions. Data from genome-wide gene expression assays can be used to estimate molecular activities of regulators within a model organism and extrapolate them to biological processes in humans. This approach is valuable in studies to...

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Autores principales:	Jianying Li, Pierre R. Bushel, Lin Lin, Kevin Day, Tianyuan Wang, Francesco J. DeMayo, San-Pin Wu, Jian-Liang Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	structural equation modeling gene expression In silico perturbation molecular interaction R Shiny Genetics QH426-470
Acceso en línea:	https://doaj.org/article/23c0123353774a9faf428f930b51a0fc
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spelling	oai:doaj.org-article:23c0123353774a9faf428f930b51a0fc2021-11-30T18:30:39ZStructural Equation Modeling of In silico Perturbations1664-802110.3389/fgene.2021.727532https://doaj.org/article/23c0123353774a9faf428f930b51a0fc2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.727532/fullhttps://doaj.org/toc/1664-8021Gene expression is controlled by multiple regulators and their interactions. Data from genome-wide gene expression assays can be used to estimate molecular activities of regulators within a model organism and extrapolate them to biological processes in humans. This approach is valuable in studies to better understand complex human biological systems which may be involved in diseases and hence, have potential clinical relevance. In order to achieve this, it is necessary to infer gene interactions that are not directly observed (i.e. latent or hidden) by way of structural equation modeling (SEM) on the expression levels or activities of the downstream targets of regulator genes. Here we developed an R Shiny application, termed “Structural Equation Modeling of In silico Perturbations (SEMIPs)” to compute a two-sided t-statistic (T-score) from analysis of gene expression data, as a surrogate to gene activity in a given human specimen. SEMIPs can be used in either correlational studies between outcome variables of interest or subsequent model fitting on multiple variables. This application implements a 3-node SEM model that consists of two upstream regulators as input variables and one downstream reporter as an outcome variable to examine the significance of interactions among these variables. SEMIPs enables scientists to investigate gene interactions among three variables through computational and mathematical modeling (i.e. in silico). In a case study using SEMIPs, we have shown that putative direct downstream genes of the GATA Binding Protein 2 (GATA2) transcription factor are sufficient to infer its activities in silico for the conserved progesterone receptor (PGR)-GATA2-SRY-box transcription factor 17 (SOX17) genetic network in the human uterine endometrium.Jianying LiJianying LiJianying LiPierre R. BushelPierre R. BushelLin LinLin LinKevin DayTianyuan WangTianyuan WangFrancesco J. DeMayoSan-Pin WuJian-Liang LiFrontiers Media S.A.articlestructural equation modelinggene expressionIn silico perturbationmolecular interactionR ShinyGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	structural equation modeling gene expression In silico perturbation molecular interaction R Shiny Genetics QH426-470
spellingShingle	structural equation modeling gene expression In silico perturbation molecular interaction R Shiny Genetics QH426-470 Jianying Li Jianying Li Jianying Li Pierre R. Bushel Pierre R. Bushel Lin Lin Lin Lin Kevin Day Tianyuan Wang Tianyuan Wang Francesco J. DeMayo San-Pin Wu Jian-Liang Li Structural Equation Modeling of In silico Perturbations
description	Gene expression is controlled by multiple regulators and their interactions. Data from genome-wide gene expression assays can be used to estimate molecular activities of regulators within a model organism and extrapolate them to biological processes in humans. This approach is valuable in studies to better understand complex human biological systems which may be involved in diseases and hence, have potential clinical relevance. In order to achieve this, it is necessary to infer gene interactions that are not directly observed (i.e. latent or hidden) by way of structural equation modeling (SEM) on the expression levels or activities of the downstream targets of regulator genes. Here we developed an R Shiny application, termed “Structural Equation Modeling of In silico Perturbations (SEMIPs)” to compute a two-sided t-statistic (T-score) from analysis of gene expression data, as a surrogate to gene activity in a given human specimen. SEMIPs can be used in either correlational studies between outcome variables of interest or subsequent model fitting on multiple variables. This application implements a 3-node SEM model that consists of two upstream regulators as input variables and one downstream reporter as an outcome variable to examine the significance of interactions among these variables. SEMIPs enables scientists to investigate gene interactions among three variables through computational and mathematical modeling (i.e. in silico). In a case study using SEMIPs, we have shown that putative direct downstream genes of the GATA Binding Protein 2 (GATA2) transcription factor are sufficient to infer its activities in silico for the conserved progesterone receptor (PGR)-GATA2-SRY-box transcription factor 17 (SOX17) genetic network in the human uterine endometrium.
format	article
author	Jianying Li Jianying Li Jianying Li Pierre R. Bushel Pierre R. Bushel Lin Lin Lin Lin Kevin Day Tianyuan Wang Tianyuan Wang Francesco J. DeMayo San-Pin Wu Jian-Liang Li
author_facet	Jianying Li Jianying Li Jianying Li Pierre R. Bushel Pierre R. Bushel Lin Lin Lin Lin Kevin Day Tianyuan Wang Tianyuan Wang Francesco J. DeMayo San-Pin Wu Jian-Liang Li
author_sort	Jianying Li
title	Structural Equation Modeling of In silico Perturbations
title_short	Structural Equation Modeling of In silico Perturbations
title_full	Structural Equation Modeling of In silico Perturbations
title_fullStr	Structural Equation Modeling of In silico Perturbations
title_full_unstemmed	Structural Equation Modeling of In silico Perturbations
title_sort	structural equation modeling of in silico perturbations
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/23c0123353774a9faf428f930b51a0fc
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_version_	1718406367436341248

Structural Equation Modeling of In silico Perturbations

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