Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments

Stefano Parodi1*, Vito Pistoia2 and Marco Muselli3

Author Affiliations

1 Epidemiology and Biostatistics Section, Scientific Directorate, G. Gaslini Children's Hospital, Genoa, Italy

2 Laboratory of Oncology, G. Gaslini Children's Hospital, Genoa, Italy

3 Institute of Electronics, Computer and Telecommunication Engineering, Italian National Research Council, Genoa, Italy

For all author emails, please log on.

BMC Bioinformatics 2008, 9:410  doi:10.1186/1471-2105-9-410

Published: 3 October 2008

Abstract

Background

Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods.

Results

We assessed the performance of our method using data from a publicly available database of 4026 genes, including 14 normal B cell samples (NBC) and 20 heterogeneous lymphomas (namely: 9 follicular lymphomas and 11 chronic lymphocytic leukemias). Moreover, NBC also included two sub-classes, i.e., 6 heavily stimulated and 8 slightly or not stimulated samples. We identified 1607 differentially expressed genes with an estimated False Discovery Rate of 15%. Among them, 16 corresponded to NPRC and all escaped standard selection procedures based on AUC and t statistics. Moreover, a simple inspection to the shape of such plots allowed to identify the two subclasses in either one class in 13 cases (81%).

Conclusion

NPRC represent a new useful tool for the analysis of microarray data.