PIntMF: Penalized Integrative Matrix Factorization Method for Multi-Omics Data

TL;DR

PIntMF is a novel penalized matrix factorization method that effectively clusters and identifies relevant variables in multi-omics data, improving interpretability and outperforming existing methods in both synthetic and real datasets.

Contribution

Introduces PIntMF, a sparse, constrained matrix factorization model with automatic tuning, enhancing multi-omics data integration and interpretation.

Findings

Successfully identifies relevant clusters and variables in synthetic data.

Reveals interpretable clusters linked to clinical data in real datasets.

Outperforms existing methods in clustering and variable selection.

Abstract

It is more and more common to explore the genome at diverse levels and not only at a single omic level. Through integrative statistical methods, omics data have the power to reveal new biological processes, potential biomarkers, and subgroups of a cohort. The matrix factorization (MF) is a unsupervised statistical method that allows giving a clustering of individuals, but also revealing relevant omic variables from the various blocks. Here, we present PIntMF (Penalized Integrative Matrix Factorization), a model of MF with sparsity, positivity and equality constraints.To induce sparsity in the model, we use a classical Lasso penalization on variable and individual matrices. For the matrix of samples, sparsity helps for the clustering, and normalization (matching an equality constraint) of inferred coefficients is added for a better interpretation. Besides, we add an automatic tuning of the sparsity parameters using the famous glmnet package. We also proposed three criteria to help the user to choose the number of latent variables. PIntMF was compared to other state-of-the-art integrative methods including feature selection techniques in both synthetic and real data. PIntMF succeeds in finding relevant clusters as well as variables in two types of simulated data (correlated and uncorrelated). Then, PIntMF was applied to two real datasets (Diet and cancer), and it reveals interpretable clusters linked to available clinical data. Our method outperforms the existing ones on two criteria (clustering and variable selection). We show that PIntMF is an easy, fast, and powerful tool to extract patterns and cluster samples from multi-omics data.