DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling.

ABSTRACT:

Motivation

Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation.

Results

DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The

SUBMITTER: Campos-Laborie FJ 

PROVIDER: S-EPMC6761977 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature