Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

Abstract Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal control...

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Autores principales:	Guini Hong, Hongdong Li, Jiahui Zhang, Qingzhou Guan, Rou Chen, Zheng Guo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/925220fe34644c3b99eab0ee501c68c8
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spelling	oai:doaj.org-article:925220fe34644c3b99eab0ee501c68c82021-12-02T12:32:24ZIdentifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings10.1038/s41598-017-01536-32045-2322https://doaj.org/article/925220fe34644c3b99eab0ee501c68c82017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01536-3https://doaj.org/toc/2045-2322Abstract Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.Guini HongHongdong LiJiahui ZhangQingzhou GuanRou ChenZheng GuoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guini Hong Hongdong Li Jiahui Zhang Qingzhou Guan Rou Chen Zheng Guo Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
description	Abstract Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.
format	article
author	Guini Hong Hongdong Li Jiahui Zhang Qingzhou Guan Rou Chen Zheng Guo
author_facet	Guini Hong Hongdong Li Jiahui Zhang Qingzhou Guan Rou Chen Zheng Guo
author_sort	Guini Hong
title	Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
title_short	Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
title_full	Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
title_fullStr	Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
title_full_unstemmed	Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
title_sort	identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/925220fe34644c3b99eab0ee501c68c8
work_keys_str_mv	AT guinihong identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings AT hongdongli identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings AT jiahuizhang identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings AT qingzhouguan identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings AT rouchen identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings AT zhengguo identifyingdiseaseassociatedpathwaysinonephenotypedatabasedonreversalgeneexpressionorderings
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Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

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