Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach

Abstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare...

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Autores principales:	Kelly M. Cahill, Zhiguang Huo, George C. Tseng, Ryan W. Logan, Marianne L. Seney
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea08d28b830a44899508fdecbeb39c8d
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spelling	oai:doaj.org-article:ea08d28b830a44899508fdecbeb39c8d2021-12-02T15:08:13ZImproved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach10.1038/s41598-018-27903-22045-2322https://doaj.org/article/ea08d28b830a44899508fdecbeb39c8d2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-27903-2https://doaj.org/toc/2045-2322Abstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap.Kelly M. CahillZhiguang HuoGeorge C. TsengRyan W. LoganMarianne L. SeneyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
description	Abstract Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap.
format	article
author	Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney
author_facet	Kelly M. Cahill Zhiguang Huo George C. Tseng Ryan W. Logan Marianne L. Seney
author_sort	Kelly M. Cahill
title	Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
title_short	Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
title_full	Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
title_fullStr	Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
title_full_unstemmed	Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
title_sort	improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/ea08d28b830a44899508fdecbeb39c8d
work_keys_str_mv	AT kellymcahill improvedidentificationofconcordantanddiscordantgeneexpressionsignaturesusinganupdatedrankrankhypergeometricoverlapapproach AT zhiguanghuo improvedidentificationofconcordantanddiscordantgeneexpressionsignaturesusinganupdatedrankrankhypergeometricoverlapapproach AT georgectseng improvedidentificationofconcordantanddiscordantgeneexpressionsignaturesusinganupdatedrankrankhypergeometricoverlapapproach AT ryanwlogan improvedidentificationofconcordantanddiscordantgeneexpressionsignaturesusinganupdatedrankrankhypergeometricoverlapapproach AT mariannelseney improvedidentificationofconcordantanddiscordantgeneexpressionsignaturesusinganupdatedrankrankhypergeometricoverlapapproach
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Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach

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