ArticleJournal of machine learning research : JMLR2021
Empirical Bayes Matrix Factorization.
Article in Journal of machine learning research : JMLR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
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- A TAD-informed aging-brain xQTL atlas of multi-modal and cell-type-resolved regulatory variation.medRxiv : the preprint server for health sciences · 2026Article
- Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.Cell genomics · 2026Article
- ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction.medRxiv : the preprint server for health sciences · 2025Article
- Sparse matrix factorization robust to sample sharing across GWASs reveals interpretable genetic components.American journal of human genetics · 2025Article
- fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025Article
- Genetic contributions to epigenetic-defined endotypes of allergic phenotypes in children.American journal of human genetics · 2025Article
- Covariate-moderated Empirical Bayes Matrix Factorization.Advances in neural information processing systems · 2025Article
- Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition.Nature genetics · 2025Article
- ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families.Journal of statistical software · 2025Article
- Empirical Bayes Linked Matrix Decomposition.Machine learning · 2024Article
- SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.Genome biology · 2024Article
- SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis.iScience · 2023Article
- Transcriptomics and chromatin accessibility in multiple African population samples.bioRxiv : the preprint server for biology · 2023Article
- The Bayesian lens and Bayesian blinkers.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- The functional and evolutionary impacts of human-specific deletions in conserved elements.Science (New York, N.Y.) · 2023Article
- Causal Discovery in High-dimensional, Multicollinear Datasets.Frontiers in epidemiology · 2022Article
- Article
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Authors and funding
2 authors.
Funding
Abstract
Matrix factorization methods, which include Factor analysis (FA) and Principal Components Analysis (PCA), are widely used for inferring and summarizing structure in multivariate data. Many such methods use a penalty or prior distribution to achieve sparse representations ("Sparse FA/PCA"), and a key question is how much sparsity to induce. Here we introduce a general Empirical Bayes approach to matrix factorization (EBMF), whose key feature is that it estimates the appropriate amount of sparsity by estimating prior distributions from the observed data. The approach is very flexible: it allows for a wide range of different prior families and allows that each component of the matrix factorization may exhibit a different amount of sparsity. The key to this flexibility is the use of a variational approximation, which we show effectively reduces fitting the EBMF model to solving a simpler problem, the so-called "normal means" problem. We demonstrate the benefits of EBMF with sparse priors through both numerical comparisons with competing methods and through analysis of data from the GTEx (Genotype Tissue Expression) project on genetic associations across 44 human tissues. In numerical comparisons EBMF often provides more accurate inferences than other methods. In the GTEx data, EBMF identifies interpretable structure that agrees with known relationships among human tissues. Software implementing our approach is available at https://github.com/stephenslab/flashr.
Indexed as
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37920532PMC10621241What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.