Linked Matrix Factorization

TL;DR

Linked Matrix Factorization (LMF) is a novel method for jointly decomposing multiple linked data matrices with shared rows and columns, enabling integrated analysis, visualization, and missing data imputation in complex multi-source datasets.

Contribution

This paper introduces LMF, a unified low-rank factorization approach for simultaneous horizontal and vertical data integration, addressing more general sharing structures among matrices.

Findings

LMF effectively decomposes shared and specific variation across matrices.

Theoretical results establish LMF's uniqueness and identifiability.

Simulation studies demonstrate LMF's accuracy and robustness.

Abstract

In recent years, a number of methods have been developed for the dimension reduction and decomposition of multiple linked high-content data matrices. Typically these methods assume that just one dimension, rows or columns, is shared among the data sources. This shared dimension may represent common features that are measured for different sample sets (i.e., horizontal integration) or a common set of samples with measurements for different feature sets (i.e., vertical integration). In this article we introduce an approach for simultaneous horizontal and vertical integration, termed Linked Matrix Factorization (LMF), for the more general situation where some matrices share rows (e.g., features) and some share columns (e.g., samples). Our motivating application is a cytotoxicity study with accompanying genomic and molecular chemical attribute data. In this data set, the toxicity matrix (cell lines $\times$ chemicals) shares its sample set with a genotype matrix (cell lines $\times$ SNPs), and shares its feature set with a chemical molecular attribute matrix (chemicals $\times$ attributes). LMF gives a unified low-rank factorization of these three matrices, which allows for the decomposition of systematic variation that is shared among the three matrices and systematic variation that is specific to each matrix. This may be used for efficient dimension reduction, exploratory visualization, and the imputation of missing data even when entire rows or columns are missing from a constituent data matrix. We present theoretical results concerning the uniqueness, identifiability, and minimal parametrization of LMF, and evaluate it with extensive simulation studies.