ArticleBMC bioinformatics2016
Separating common from distinctive variation.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.PloS one · 2026Article
- Causal effects of gut microbiota on gout and hyperuricemia: insights from genome-wide Mendelian randomization, RNA-sequencing, 16S rRNA sequencing, and metabolomes.Bioscience reports · 2024Article
- Reposition: Focalizing β-Alanine Metabolism and the Anti-Inflammatory Effects of Its Metabolite Based on Multi-Omics Datasets.International journal of molecular sciences · 2024Article
- CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.Electronic journal of statistics · 2022Article
- D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.Journal of machine learning research : JMLR · 2022Article
- STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.Frontiers in genetics · 2021Article
- Linking Plant Secondary Metabolites and Plant Microbiomes: A Review.Frontiers in plant science · 2021Review
- Article
- D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets.Journal of the American Statistical Association · 2020Article
- Common and distinct variation in data fusion of designed experimental data.Metabolomics : Official journal of the Metabolomic Society · 2019Article
- Integrating omics datasets with the OmicsPLS package.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundJoint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation.
resultsThe methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods.
conclusionsAll three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.
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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.