Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes

Abstract Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered...

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Autores principales:	Kourosh Zarringhalam, David Degras, Christoph Brockel, Daniel Ziemek
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f44a5a3539404d7d921e917d3bb63b7d
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spelling	oai:doaj.org-article:f44a5a3539404d7d921e917d3bb63b7d2021-12-02T15:08:34ZRobust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes10.1038/s41598-018-19635-02045-2322https://doaj.org/article/f44a5a3539404d7d921e917d3bb63b7d2018-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-19635-0https://doaj.org/toc/2045-2322Abstract Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.Kourosh ZarringhalamDavid DegrasChristoph BrockelDaniel ZiemekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kourosh Zarringhalam David Degras Christoph Brockel Daniel Ziemek Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
description	Abstract Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.
format	article
author	Kourosh Zarringhalam David Degras Christoph Brockel Daniel Ziemek
author_facet	Kourosh Zarringhalam David Degras Christoph Brockel Daniel Ziemek
author_sort	Kourosh Zarringhalam
title	Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
title_short	Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
title_full	Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
title_fullStr	Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
title_full_unstemmed	Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
title_sort	robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/f44a5a3539404d7d921e917d3bb63b7d
work_keys_str_mv	AT kouroshzarringhalam robustphenotypepredictionfromgeneexpressiondatausingdifferentialshrinkageofcoregulatedgenes AT daviddegras robustphenotypepredictionfromgeneexpressiondatausingdifferentialshrinkageofcoregulatedgenes AT christophbrockel robustphenotypepredictionfromgeneexpressiondatausingdifferentialshrinkageofcoregulatedgenes AT danielziemek robustphenotypepredictionfromgeneexpressiondatausingdifferentialshrinkageofcoregulatedgenes
_version_	1718388099308847104

Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes

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