Optimal separation of high dimensional transcriptome for complex multigenic traits

Abstract

The plight of navigating high-dimensional transcription datasets remains a persistent problem. This problem is further amplified for complex disorders, such as cancer, as these disorders are often multigenic traits with multiple subsets of genes collectively affecting the type, stage, and severity of the trait. We are often faced with a trade-off between reducing the dimensionality of our datasets and maintaining the integrity of our data. Almost exclusively, researchers apply techniques commonly known as dimensionality reduction to reduce the dimensions of the feature space to allow classifiers to work in more appropriately sized input spaces. As the number of dimensions is reduced, however, the ability to distinguish classes from one another reduces as well. Thus, to accomplish both tasks simultaneously for very high dimensional transcriptome for complex multigenic traits, we propose a new supervised technique, Class Separation Transformation (CST). CST accomplishes both tasks simultaneously by significantly reducing the dimensionality of the input space into a one-dimensional transformed space that provides optimal separation between the differing classes. We compare our method with existing state-of-the-art methods using both real and synthetic datasets, demonstrating that CST is the more accurate, robust, and scalable technique relative to existing methods. Code used in this paper is available on https://github.com/aisharjya/CST