Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

• Published in: BMC cancer Vol. 10; no. 1; p. 532
• Main Authors: Lauss, Martin, Frigyesi, Attila, Ryden, Tobias, Höglund, Mattias
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.10.2010; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Area Under Curve; Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Bladder; Cancer; Cancer and Oncology; Cancer och onkologi; Cancer Research; Clinical Medicine; Computational Biology - methods; Databases, Genetic; Diagnosis; Discriminant analysis; Gene expression; Gene Expression Regulation; Genetic algorithms; Genetic aspects; Genome-Wide Association Study; Genomics - methods; Health Promotion and Disease Prevention; Humans; Identification and classification; Klinisk medicin; Lung cancer; Medical and Health Sciences; Medicin och hälsovetenskap; Medicine/Public Health; Microarrays; Models, Statistical; Neural Networks (Computer); Oncology; Pattern Recognition, Automated; post-genomic analysis and emerging technologies; Programming languages; Reproducibility of Results; ROC Curve; Software; Standard deviation; Studies; Surgical Oncology; Survival; Systems biology; Tumor staging; Urinary Bladder Neoplasms - genetics; Feature Selection; Receiver Operating Characteristic Analysis; Breast Cancer Dataset; Support Vector Machine; Linear Discriminant Analysis
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/1471-2407-10-532

Background Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms. Results The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies. Conclusions We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html .