ArticleBiometrics2020
Integrative factorization of bidimensionally linked matrices.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Empirical Bayes Linked Matrix Decomposition.Machine learning · 2024Article
- Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.Computational statistics & data analysis · 2024Article
- Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.bioRxiv : the preprint server for biology · 2024Article
- RELIEF: A structured multivariate approach for removal of latent inter-scanner effects.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- HIGH-DIMENSIONAL FACTOR REGRESSION FOR HETEROGENEOUS SUBPOPULATIONS.Statistica Sinica · 2023Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
- Two-stage linked component analysis for joint decomposition of multiple biologically related data sets.Biostatistics (Oxford, England) · 2022Article
- BIDIMENSIONAL LINKED MATRIX FACTORIZATION FOR PAN-OMICS PAN-CANCER ANALYSIS.The annals of applied statistics · 2022Article
- Bayesian Distance Weighted Discrimination.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
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Authors and funding
2 authors.
Funding
Abstract
Advances in molecular "omics" technologies have motivated new methodologies for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (eg, multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose bidimensional integrative factorization (BIDIFAC) for integrative dimension reduction and signal approximation of bidimensionally linked data matrices. Our method factorizes data into (a) globally shared, (b) row-shared, (c) column-shared, and (d) single-matrix structural components, facilitating the investigation of shared and unique patterns of variability. For estimation, we use a penalized objective function that extends the nuclear norm penalization for a single matrix. As an alternative to the complicated rank selection problem, we use results from the random matrix theory to choose tuning parameters. We apply our method to integrate two genomics platforms (messenger RNA and microRNA expression) across two sample cohorts (tumor samples and normal tissue samples) using the breast cancer data from the Cancer Genome Atlas. We provide R code for fitting BIDIFAC, imputing missing values, and generating simulated data.
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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.