Wave-LSTM: Multi-scale analysis of somatic whole genome copy number profiles

TL;DR

Wave-LSTM introduces a multi-scale analysis method combining wavelet decomposition and deep learning to better understand complex copy number profiles in cancer genomes, aiding in sub-clonal structure detection and survival prediction.

Contribution

The paper presents Wave-LSTM, a novel approach that captures multi-scale features of genome copy number data using wavelet-based source separation and attention mechanisms.

Findings

Effective in deciphering sub-clonal structures from single-cell data

Improves survival prediction accuracy in tumor profiles

Captures multi-scale genomic alterations

Abstract

Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers. This genomic complexity is known to be associated with poorer outcomes for patients but describing its contribution in detail has been difficult. Copy number alterations can affect large regions spanning whole chromosomes or the entire genome itself but can also be localised to only small segments of the genome and no methods exist that allow this multi-scale nature to be quantified. In this paper, we address this using Wave-LSTM, a signal decomposition approach designed to capture the multi-scale structure of complex whole genome copy number profiles. Using wavelet-based source separation in combination with deep learning-based attention mechanisms. We show that Wave-LSTM can be used to derive multi-scale representations from copy number profiles which can be used to decipher sub-clonal structures from single-cell copy number data and to improve survival prediction performance from patient tumour profiles.