Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.

Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can b...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Maike Ahrens, Michael Turewicz, Swaantje Casjens, Caroline May, Beate Pesch, Christian Stephan, Dirk Woitalla, Ralf Gold, Thomas Brüning, Helmut E Meyer, Jörg Rahnenführer, Martin Eisenacher
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cbf81d05f4f24ccf8e5cf561a07e8ead
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cbf81d05f4f24ccf8e5cf561a07e8ead
record_format	dspace
spelling	oai:doaj.org-article:cbf81d05f4f24ccf8e5cf561a07e8ead2021-11-18T08:45:05ZDetection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.1932-620310.1371/journal.pone.0079380https://doaj.org/article/cbf81d05f4f24ccf8e5cf561a07e8ead2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24278130/?tool=EBIhttps://doaj.org/toc/1932-6203Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including outlier sum, PADGE, and kurtosis. We also propose the new FisherSum score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and t-test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method.Maike AhrensMichael TurewiczSwaantje CasjensCaroline MayBeate PeschChristian StephanDirk WoitallaRalf GoldThomas BrüningHelmut E MeyerJörg RahnenführerMartin EisenacherPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79380 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maike Ahrens Michael Turewicz Swaantje Casjens Caroline May Beate Pesch Christian Stephan Dirk Woitalla Ralf Gold Thomas Brüning Helmut E Meyer Jörg Rahnenführer Martin Eisenacher Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
description	Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including outlier sum, PADGE, and kurtosis. We also propose the new FisherSum score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and t-test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method.
format	article
author	Maike Ahrens Michael Turewicz Swaantje Casjens Caroline May Beate Pesch Christian Stephan Dirk Woitalla Ralf Gold Thomas Brüning Helmut E Meyer Jörg Rahnenführer Martin Eisenacher
author_facet	Maike Ahrens Michael Turewicz Swaantje Casjens Caroline May Beate Pesch Christian Stephan Dirk Woitalla Ralf Gold Thomas Brüning Helmut E Meyer Jörg Rahnenführer Martin Eisenacher
author_sort	Maike Ahrens
title	Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
title_short	Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
title_full	Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
title_fullStr	Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
title_full_unstemmed	Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
title_sort	detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/cbf81d05f4f24ccf8e5cf561a07e8ead
work_keys_str_mv	AT maikeahrens detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT michaelturewicz detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT swaantjecasjens detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT carolinemay detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT beatepesch detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT christianstephan detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT dirkwoitalla detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT ralfgold detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT thomasbruning detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT helmutemeyer detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT jorgrahnenfuhrer detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures AT martineisenacher detectionofpatientsubgroupswithdifferentialexpressioninomicsdataacomprehensivecomparisonofunivariatemeasures
_version_	1718421410372648960

Detection of patient subgroups with differential expression in omics data: a comprehensive comparison of univariate measures.

Ejemplares similares