Angle-based joint and individual variation explained

  title={Angle-based joint and individual variation explained},
  author={Qing Feng and Meilei Jiang and Jan Hannig and J. S. Marron},
  journal={J. Multivar. Anal.},
Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes
The method developed is driven by how functionally correlated brain structures vary together between autism and control groups and is effective, robust, and interpretable in recognizing the underlying patterns of the joint variation of multiblock non-Euclidean data.
MM-PCA: Integrative Analysis of Multi-group and Multi-view Data.
The method, Multi-group Multi-view Principal Component Analysis (MM-PCA), identifies partially shared, sparse low-rank components that results in an integrative bi-clustering across cohorts and views.
Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets
A method called two-stage linked component analysis (2s-LCA) is proposed to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition.
Covariate‐driven factorization by thresholding for multiblock data
The proposed factorization provides accurate estimation of individual and (partially) joint structures in multiblock data, as confirmed by simulation studies and it is demonstrated that the estimated block structures provide straightforward interpretation and facilitate subsequent analyses.
Bayesian time-aligned factor analysis of paired multivariate time series
A Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) is proposed that includes uncertainty in time alignment through an unknown warping function and enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm.
sJIVE: Supervised Joint and Individual Variation Explained
Structural learning and integrative decomposition of multi‐view data
A novel linked component model that directly incorporates partially‐shared structures is formulated that allows for joint identification of the number of components of each type, in contrast to existing sequential approaches.
RaJIVE: Robust Angle Based JIVE for Integrating Noisy Multi-Source Data
A robust extension of aJIVE (RaJIVE) is proposed that integrates a robust formulation of the singular value decomposition into the aJive approach and is shown to provide correct decompositions even in the presence of outliers and improves the performance of a JIVE.
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multiple High-dimensional Datasets
This work proposes decomposition-based generalized canonical correlation analysis (D-GCCA), a novel decomposition method that appropriately defines matrices on the L2 space of random variables, whereas most existing methods are developed on its approximation, the Euclidean dot product space.
Integrative analysis of multi-omics data improves model predictions: an application to lung cancer
This work shows how an integrative analysis that preserves both components of variation is more appropriate than analyses considering uniquely individual or joint components, and shows how both joint and individual components contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes.


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.
Bayesian consensus clustering
A computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings is described and demonstrated that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source independently.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Comprehensive experimental results on both the synthetic and real-world data demonstrate significant advantages of the proposed CIFE method in comparison with the state-of-the-art.
R.JIVE for exploration of multi-source molecular data
R.JIVE is introduced, an intuitive R package to perform JIVE and visualize the results, and several improvements and extensions of the JIVE methodology that are included are discussed.
Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE).
A recent method is applied, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources, to show the applicability of JIVE for the simultaneous analysis of multi-source data.
Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
This paper presents Sparse Canonical Correlation Analysis (SCCA) which examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsetsOf variables of different types while performing variable selection.
JIVE integration of imaging and behavioral data
Genome-wide sparse canonical correlation of gene expression with genotypes
Sparse canonical correlation analysis is introduced, which examines the relationships of many genetic loci and gene expression phenotypes by providing sparse linear combinations that include only a small subset of loci & phenotypes that are sufficiently small for biological interpretability and further investigation.
Relations Between Two Sets of Variates
Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at
Sparse canonical methods for biological data integration: application to a cross-platform study
PLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines and outperformed CIA that tended to select redundant information.