Unsupervised gene identification in colorectal cancer

Abstract

Cancer is a large family of genetic diseases that involve abnormal cell growth. Genetic mutations can vary from one patient to another. Therefore, personalized precision is required to increase the reliability of prognostic predictions and the benefit of therapeutic decisions. The most important issues concerning gene analysis are strong noise, high dimensionality and minor differences between observations. Therefore, it has been chosen an unsupervised approach in order to bypass the high dimensionality issue using parallel coordinates and a scoring algorithm of features based on their clustering ability. Traditional methods of dimensionality reduction and projection are here used on subset features with high discriminant power in order to better analyze the data manifold and select the more meaningful genes. Previous studies show that mutations of genes NRAS, KRAS and BRAF lead to a dramatic decrease in therapeutic effectiveness. The following analysis tries to explore in an unconventional way gene expressions over tissues which are wild type regarding to these genes.