PIntMF: Penalized Integrative Matrix Factorization method for Multi-omics data.

ABSTRACT: It is more and more common to perform multi-omics analyses to explore the genome at diverse levels and not only at a single level. Through integrative statistical methods, multi-omics data have the power to reveal new biological processes, potential biomarkers, and subgroups in a cohort. Matrix factorization (MF) is an unsupervised statistical method that allows a clustering of individuals, but also reveals relevant omics variables from the various blocks. Here, we present PIntMF (Penalized Integrative Matrix Factorization), an MF model with sparsity, positivity and equality constraints. To induce sparsity in the model, we used a classical Lasso penalization on variable and individual matrices. For the matrix of samples, sparsity helps in the clustering, while normalization (matching an eq

SUBMITTER: Pierre-Jean M 

PROVIDER: S-EPMC8796362 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature