A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification

TL;DR

This paper introduces a deep learning framework that uses a class-conditional variational autoencoder to generate synthetic gene expression data, significantly improving pan-cancer classification accuracy from RNA-Seq data.

Contribution

The study presents a novel cVAE-based data augmentation method that enhances deep learning models for pan-cancer RNA-Seq classification, addressing high dimensionality and class imbalance.

Findings

Achieved approximately 98% classification accuracy on test data.

Synthetic data augmentation improved performance, especially for underrepresented classes.

Demonstrated the effectiveness of cVAE in generating realistic gene expression samples.

Abstract

Pan-cancer classification using transcriptomic (RNA-Seq) data can inform tumor subtyping and therapy selection, but is challenging due to extremely high dimensionality and limited sample sizes. In this study, we propose a novel deep learning framework that uses a class-conditional variational autoencoder (cVAE) to augment training data for pan-cancer gene expression classification. Using 801 tumor RNA-Seq samples spanning 5 cancer types from The Cancer Genome Atlas (TCGA), we first perform feature selection to reduce 20,531 gene expression features to the 500 most variably expressed genes. A cVAE is then trained on this data to learn a latent representation of gene expression conditioned on cancer type, enabling the generation of synthetic gene expression samples for each tumor class. We augment the training set with these cVAE-generated samples (doubling the dataset size) to mitigate overfitting and class imbalance. A two-layer multilayer perceptron (MLP) classifier is subsequently trained on the augmented dataset to predict tumor type. The augmented framework achieves high classification accuracy (~98%) on a held-out test set, substantially outperforming a classifier trained on the original data alone. We present detailed experimental results, including VAE training curves, classifier performance metrics (ROC curves and confusion matrix), and architecture diagrams to illustrate the approach. The results demonstrate that cVAE-based synthetic augmentation can significantly improve pan-cancer prediction performance, especially for underrepresented cancer classes.