ArticleThe annals of applied statistics2013
JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.
Article in The annals of applied statistics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers, 1 of them a synthesis that pooled it.
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Who cites it
254 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of Diurnal Transcriptomics in Mouse Liver Reveals Low Repeatability of Rhythm Analyses.Journal of biological rhythms · 2023Pooled it
- Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments.bioRxiv : the preprint server for biology · 2026Article
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.International journal of molecular sciences · 2026Article
- DIVAS: an R package for identifying shared and individual variations of multiomics data.Bioinformatics (Oxford, England) · 2026Article
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration.bioRxiv : the preprint server for biology · 2026Article
- Age-stratified individual-specific subspace of autism spectrum disorder based on common orthogonal basis extraction algorithm improves the accuracy of clinical symptoms prediction.BMC psychiatry · 2026Article
- Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Contrastive Dimension Reduction: A Systematic Review.Wiley interdisciplinary reviews. Computational statistics · 2026Article
- Multimodal axes reveal individualized amyloid-medRxiv : the preprint server for health sciences · 2026Article
- ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline.Cell reports methods · 2026Article
- Bayesian joint and individual component regression for multigroup physiological data.Scientific reports · 2026Article
- A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.Statistics in medicine · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.Statistics in medicine · 2026Article
- Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- A fast integrative clustering and feature selection approach for high-dimensional multiview data.Statistical methods in medical research · 2026Article
- Hierarchical structure-guided high-dimensional multi-view clustering.Journal of multivariate analysis · 2026Article
- ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.PloS one · 2026Article
194 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
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Abstract
Research in several fields now requires the analysis of datasets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types.
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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.