Blind source separation methods for deconvolution of complex signals in cancer biology

TL;DR

This paper reviews blind source separation techniques, specifically ICA and NMF, highlighting their applications in analyzing complex gene expression data in cancer biology and comparing them to traditional methods.

Contribution

It provides a comprehensive overview of ICA and NMF methods, their implementation, advantages, and software tools for analyzing cancer-related gene expression data.

Findings

ICA and NMF are effective for complex signal deconvolution in cancer biology.

These methods offer advantages over traditional statistical approaches.

Several software tools are available for implementing these methods.

Abstract

Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.