Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

Abstract

SummaryHigh-dimensional data is currently standard for biological inquiry. Biological systems are comprised of interrelated gene regulatory mechanisms, gene-gene interactions, and cellular interactions. These interactions induce low-dimensional structure within the high-dimensional data. Matrix factorization, also known as compressed sensing, learns low-dimensional mathematical representations from high-dimensional data. These factorization techniques can embed assumptions about pleiotropy, epistasis, inter-relationships between complex traits, and context-dependent interactions. They have been applied to uncover new biological knowledge in a breadth of topics ranging from pathway discovery to time course analysis. These techniques have been applied to data from diverse high-throughput omics technologies, including bulk and single-cell data. There are numerous computational techniques within the class of matrix factorization, each of which provides a unique interpretation of the processes in high-dimensional data. We review the visualization and applications of matrix factorization to systems-level analyses, which are diverse and require standardization to enable biological interpretation. Codifying the techniques to decipher biologically relevant features with matrix factorization enables their broad application to discovery beyond the limits of current biological knowledge-answering questions from high-dimensional data that we have not yet thought to ask.

MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease

“…There are also technical considerations, as measuring combinations of genes will reduce the multiple hypothesis testing burden, can be useful for feature engineering, and is more likely to lead to more robust results than analyses of individual gene measurements (Cleary et al, 2017). These methods have advantages over two-group gene set-based comparisons because they provide more context for genes, are better fit to the underlying data, and remove the difficulty of identifying the most useful comparisons a priori (Stein-O'Brien et al, 2017a). Unsupervised machine learning (ML) methods including matrix factorization-and autoencoder-based approaches have successfully extracted biologically meaningful low-dimensional representations of gene expression data that can distinguish disease types, predict drug response, and identify new pathway regulators (Dincer et al, 2018;Stein-O'Brien et al, 2017b;Tan et al, 2017;Way and Greene, 2017).…”

Simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization

“…GRiNCH is based on non-negative matrix factorization (NMF), a powerful dimensionality reduction method used to recover interpretable low-dimensional structure from high-dimensional datasets [32][33][34].…”