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BioMed Research International
Volume 2015, Article ID 462549, 13 pages
Research Article

Identifying and Assessing Interesting Subgroups in a Heterogeneous Population

1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
2Department of Statistics, Inha University, Incheon 402-751, Republic of Korea
3Department of Microbiology, Tumour and Cell Biology, Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institutet, 17177 Stockholm, Sweden
4Department of Oncology and Pathology, Science for Life Laboratory, Karolinska Institutet, 17121 Solna, Sweden
5Genomics, Institut Gustave Roussy, F-94805 Villejuif, France

Received 10 November 2014; Revised 1 March 2015; Accepted 3 March 2015

Academic Editor: Kristel van Steen

Copyright © 2015 Woojoo Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as k-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided.