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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: Liu, Chen, Cai, Dehan, Zeng, WuCha, Huang, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632038/
https://www.ncbi.nlm.nih.gov/pubmed/34858477
http://dx.doi.org/10.3389/fgene.2021.760155
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author Liu, Chen
Cai, Dehan
Zeng, WuCha
Huang, Yun
author_facet Liu, Chen
Cai, Dehan
Zeng, WuCha
Huang, Yun
author_sort Liu, Chen
collection PubMed
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.
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spelling pubmed-86320382021-12-01 Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge Liu, Chen Cai, Dehan Zeng, WuCha Huang, Yun Front Genet Genetics 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. Frontiers Media S.A. 2021-11-11 /pmc/articles/PMC8632038/ /pubmed/34858477 http://dx.doi.org/10.3389/fgene.2021.760155 Text en Copyright © 2021 Liu, Cai, Zeng and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Chen
Cai, Dehan
Zeng, WuCha
Huang, Yun
Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title 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_short Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge
title_sort inferring differential networks by integrating gene expression data with additional knowledge
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632038/
https://www.ncbi.nlm.nih.gov/pubmed/34858477
http://dx.doi.org/10.3389/fgene.2021.760155
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