Pattern Analysis in DNA Microarray Data through PCA-Based Gene Selection

TL;DR: This paper proposes a new methodology that is based on the application of Principal Component Analysis and other statistical tools to gain insight in the identification of relevant genes and shows that it is possible to reduce considerably the number of genes while increasing the performance of well known classifiers.

Abstract: DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. In this paper, we propose a new methodology that is based on the application of Principal Component Analysis and other statistical tools to gain insight in the identification of relevant genes. We run the approaches using two benchmark datasets: Leukemia and Lymphoma. The results show that it is possible to reduce considerably the number of genes while increasing the performance of well known classifiers.