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Decomposing variation in heterogeneous clinical omic data

AutorCampos-Laborie, Francisco J.; Sanchez-Santos, Jose Manuel; De Las Rivas, Javier
Fecha de publicación2016
EditorSociedad Española de Bioquímica y Biología Molecular
CitaciónXXXIX Congreso de la SEBBM (2016)
ResumenIndividual diversity is one of the most complex issues to deal with in omic studies of large populations. Most of the current approaches to detect differences using new generation omic-wide data are based on the analyses of significant mean or median changes that reflect average alterations in the whole population studied versus their controls or references. In this situation the biomarkers that are only related to a subset of samples are difficult to detect and often wrongly assigned, but in many occasions such sample subsets are quite relevant for the biological study performed. Considering that technical and batch-associated variations are mostly treated and corrected by robust normalization methods developed in recent years (RUV, SVA) (PMIDs: 25150836, 22257669), we explore different methodologies to understand the frequent heterogeneity in disease sample sets that come from specific clinical or biological features only associated to a subset of the population. Moreover, the classical normalization approaches often apply ways to reduce or remove unwanted variation (PMID: 26286812), but our scope is not to decrease or alter the sample signals but to identify omic biomarkers that are modified only in a sub-population of the clinical cohorts studied. The first method proposing the identification of disease outliers using genomic data was COPA (PMID: 16895932), and several other more recent approaches have tried to tackle this problem. One efficient approach to identify features associated to subsets of a population can be to explore in a recursive way the existing dependent relationships between features and samples provided by large-scale omic datasets. This type of comprehensive omic data (genomic, transcriptomic, etc) can facilitate the finding of new significant biomarkers related to hidden or not clear phenotypic conditions. The stratification of patients according to their omic profiles achieved using recursive heuristic methods is a way that we propose to assign new biomarker features between closely related states and to subset samples depending on their omic patterns.
DescripciónResumen del póster presentado al XXXIX Congreso de la Sociedad Española de Bioquímica y Biología Molecular, celebrado en Salamanca del 5 al 8 de septiembre de 2016.
URIhttp://hdl.handle.net/10261/169557
Aparece en las colecciones: (IBMCC) Comunicaciones congresos
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