Gene selection and classification of microarray data using random forest

TLDR

Gene selection for microarray classification aims to identify minimal gene sets that maintain predictive accuracy, but many existing methods rely on univariate rankings, arbitrary thresholds, and are limited to two‑class problems; random forest, however, excels with high‑dimensional, multi‑class data and provides variable importance measures. The study seeks to evaluate random forest’s performance on microarray data and its utility for gene selection. The authors applied random forest to multi‑class microarray classification and introduced a novel gene‑selection method leveraging the algorithm’s variable importance. Random forest matched or outperformed DLDA, KNN, and SVM on simulated and nine real datasets, and the new gene‑selection approach produced smaller gene sets while preserving accuracy, suggesting it should become a standard tool.

Abstract

Abstract Background Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. Results We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Conclusion Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.