Multivariate and Dimensionality-Reduction-Based Machine Learning Techniques for Tumor Classification of RNA-Seq Data

Abstract

Cancer, a genetic disease, is considered one of the leading causes of death globally and affects people of all ages. Ribonucleic acid sequencing (RNA-Seq) is a technique used to quantify the expression of genes of interest and can be used to classify cancer tumor types. This paper describes a machine learning technique to classify cancer tissue samples by tumor type, such as breast cancer, lung cancer, colon cancer, and others. More than 60,000 RNA-Seq features were analyzed using six different machine learning classification algorithms, both individually and as an ensemble. Numerous dimensionality reduction techniques addressed the challenges of working with enormous amounts of genetic data. In particular, we were able to reduce the number of features from over 60,000 to 660 in the random forest feature selection and to 68 factor features using factor analysis with an accuracy of 99% in classifying tumor types.