The efficacy of various machine learning models for multi-class classification of RNA-seq expression data

TL;DR

This study evaluates five machine learning models for multi-class cancer classification using RNA-seq data, finding ensemble methods achieve near-perfect accuracy across most cancer types, even with reduced gene features.

Contribution

It systematically compares multiple machine learning algorithms for RNA-seq based cancer classification, highlighting the superior performance of ensemble methods.

Findings

Ensemble algorithms achieve 100% accuracy on 14 of 17 cancer types.

Feature reduction to 20 genes maintains over 95% accuracy with ensembles.

Clustering and classification models perform poorly due to dataset noise.

Abstract

Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing need for accurate methods that enable early diagnosis and cover a broad range of cancers. The use of machine learning and RNA-seq expression analysis has shown promise in the classification of cancer type. However, research is inconclusive about which type of machine learning models are optimal. The suitability of five algorithms were assessed for the classification of 17 different cancer types. Each algorithm was fine-tuned and trained on the full array of 18,015 genes per sample, for 4,221 samples (75 % of the dataset). They were then tested with 1,408 samples (25 % of the dataset) for which cancer types were withheld to determine the accuracy of prediction. The results show that ensemble algorithms achieve 100% accuracy in the classification of 14 out of 17 types of cancer. The clustering and classification models, while faster than the ensembles, performed poorly due to the high level of noise in the dataset. When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.