JIVE

Joint and Individual Variation Explained (JIVE) for integrated analysis of multiple data types. Research in several fields now requires the analysis of data sets 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 data sets. 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 structures. 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. par Data and software are available at https://genome.unc.edu/jive/.


References in zbMATH (referenced in 12 articles )

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  1. Godichon-Baggioni, Antoine; Maugis-Rabusseau, Cathy; Rau, Andrea: Multiview cluster aggregation and splitting, with an application to multiomic breast cancer data (2020)
  2. Shu, Hai; Wang, Xiao; Zhu, Hongtu: D-CCA: a decomposition-based canonical correlation analysis for high-dimensional datasets (2020)
  3. Huang, Lei; Bai, Jiawei; Ivanescu, Andrada; Harris, Tamara; Maurer, Mathew; Green, Philip; Zipunnikov, Vadim: Multilevel matrix-variate analysis and its application to accelerometry-measured physical activity in clinical populations (2019)
  4. Marron, J. S.: Discussion: “A spatial modeling approach for linguistic object data: analysing dialect sound variations across Great Britain” (2019)
  5. Marron, J. S.: Comments on “Data science, big data and statistics” (2019)
  6. Wang, Miaoyan; Fischer, Jonathan; Song, Yun S.: Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition (2019)
  7. Aoshima, Makoto; Shen, Dan; Shen, Haipeng; Yata, Kazuyoshi; Zhou, Yi-Hui; Marron, J. S.: A survey of high dimension low sample size asymptotics (2018)
  8. Feng, Qing; Jiang, Meilei; Hannig, Jan; Marron, J. S.: Angle-based joint and individual variation explained (2018)
  9. Jung, Sungkyu: Continuum directions for supervised dimension reduction (2018)
  10. Li, Gen; Gaynanova, Irina: A general framework for association analysis of heterogeneous data (2018)
  11. Li, Gen; Yang, Dan; Nobel, Andrew B.; Shen, Haipeng: Supervised singular value decomposition and its asymptotic properties (2016)
  12. Lock, Eric F.; Hoadley, Katherine A.; Marron, J. S.; Nobel, Andrew B.: Joint and Individual Variation Explained (JIVE) for integrated analysis of multiple data types (2013)