Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge

Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computation...

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Autores principales:	Chen Liu, Dehan Cai, WuCha Zeng, Yun Huang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	single-cell RNA sequencing differential network analysis prior information graphical model gene regulatory network Genetics QH426-470
Acceso en línea:	https://doaj.org/article/22efb4edfdb342bf8f2d7afead8c5f07
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spelling	oai:doaj.org-article:22efb4edfdb342bf8f2d7afead8c5f072021-11-11T07:43:00ZInferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge1664-802110.3389/fgene.2021.760155https://doaj.org/article/22efb4edfdb342bf8f2d7afead8c5f072021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.760155/fullhttps://doaj.org/toc/1664-8021Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computational approaches. However, the distribution diversity of multi-platform gene expression data and the sparseness and high noise rate of single-cell RNA sequencing (scRNA-seq) data raise new challenges for existing differential network estimation methods. Furthermore, most existing methods are purely rely on gene expression data, and ignore the additional information provided by various existing biological knowledge. In this study, to address these challenges, we propose a general framework, named weighted joint sparse penalized D-trace model (WJSDM), to infer differential gene networks by integrating multi-platform gene expression data and multiple prior biological knowledge. Firstly, a non-paranormal graphical model is employed to tackle gene expression data with missing values. Then we propose a weighted group bridge penalty to integrate multi-platform gene expression data and various existing biological knowledge. Experiment results on synthetic data demonstrate the effectiveness of our method in inferring differential networks. We apply our method to the gene expression data of ovarian cancer and the scRNA-seq data of circulating tumor cells of prostate cancer, and infer the differential network associated with platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer. By analyzing the estimated differential networks, we find some important biological insights about the mechanisms underlying platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer.Chen LiuDehan CaiWuCha ZengYun HuangFrontiers Media S.A.articlesingle-cell RNA sequencingdifferential network analysisprior informationgraphical modelgene regulatory networkGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	single-cell RNA sequencing differential network analysis prior information graphical model gene regulatory network Genetics QH426-470
spellingShingle	single-cell RNA sequencing differential network analysis prior information graphical model gene regulatory network Genetics QH426-470 Chen Liu Dehan Cai WuCha Zeng Yun Huang Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
description	Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computational approaches. However, the distribution diversity of multi-platform gene expression data and the sparseness and high noise rate of single-cell RNA sequencing (scRNA-seq) data raise new challenges for existing differential network estimation methods. Furthermore, most existing methods are purely rely on gene expression data, and ignore the additional information provided by various existing biological knowledge. In this study, to address these challenges, we propose a general framework, named weighted joint sparse penalized D-trace model (WJSDM), to infer differential gene networks by integrating multi-platform gene expression data and multiple prior biological knowledge. Firstly, a non-paranormal graphical model is employed to tackle gene expression data with missing values. Then we propose a weighted group bridge penalty to integrate multi-platform gene expression data and various existing biological knowledge. Experiment results on synthetic data demonstrate the effectiveness of our method in inferring differential networks. We apply our method to the gene expression data of ovarian cancer and the scRNA-seq data of circulating tumor cells of prostate cancer, and infer the differential network associated with platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer. By analyzing the estimated differential networks, we find some important biological insights about the mechanisms underlying platinum resistance of ovarian cancer and anti-androgen resistance of prostate cancer.
format	article
author	Chen Liu Dehan Cai WuCha Zeng Yun Huang
author_facet	Chen Liu Dehan Cai WuCha Zeng Yun Huang
author_sort	Chen Liu
title	Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_short	Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_full	Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_fullStr	Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_full_unstemmed	Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_sort	inferring differential networks by integrating gene expression data with additional knowledge
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/22efb4edfdb342bf8f2d7afead8c5f07
work_keys_str_mv	AT chenliu inferringdifferentialnetworksbyintegratinggeneexpressiondatawithadditionalknowledge AT dehancai inferringdifferentialnetworksbyintegratinggeneexpressiondatawithadditionalknowledge AT wuchazeng inferringdifferentialnetworksbyintegratinggeneexpressiondatawithadditionalknowledge AT yunhuang inferringdifferentialnetworksbyintegratinggeneexpressiondatawithadditionalknowledge
_version_	1718439380544126976

Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge

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