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 method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor-response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification.