A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation

TL;DR

This paper introduces a novel deep embedded clustering method that combines autoencoder-based dimensionality reduction with soft-assignment clustering for accurate breast cancer classification using DNA methylation data.

Contribution

The study presents an end-to-end deep learning approach that integrates dimensionality reduction and clustering specifically for DNA methylation analysis, outperforming existing methods.

Findings

Achieved 99.27% clustering accuracy on breast tissue samples.

Outperformed state-of-the-art methods in DNA methylation-based breast cancer classification.

Demonstrated robustness across different methylation datasets.

Abstract

Epigenetic alterations have an important role in the development of several types of cancer. Epigenetic studies generate a large amount of data, which makes it essential to develop novel models capable of dealing with large-scale data. In this work, we propose a deep embedded refined clustering method for breast cancer differentiation based on DNA methylation. In concrete, the deep learning system presented here uses the levels of CpG island methylation between 0 and 1. The proposed approach is composed of two main stages. The first stage consists in the dimensionality reduction of the methylation data based on an autoencoder. The second stage is a clustering algorithm based on the soft-assignment of the latent space provided by the autoencoder. The whole method is optimized through a weighted loss function composed of two terms: reconstruction and classification terms. To the best of the authors' knowledge, no previous studies have focused on the dimensionality reduction algorithms linked to classification trained end-to-end for DNA methylation analysis. The proposed method achieves an unsupervised clustering accuracy of 0.9927 and an error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the deep-learning-based method using a different methylation database, an accuracy of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples is obtained. Based on these results, the proposed algorithm outperforms other state-of-the-art methods evaluated under the same conditions for breast cancer classification based on DNA methylation data.