Robust integrative biclustering for multi-view data

Weijie Zhang, Christine Wendt, Russel Bowler, Craig P. Hersh, Sandra E. Safo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In many biomedical research, multiple views of data (e.g. genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized by specific groups of variables. Biclustering methods are well-suited for this problem as they assume that specific groups of variables might be relevant only to specific groups of samples. Many biclustering methods exist for detecting row–column clusters in a view but few methods exist for data from multiple views. The few existing algorithms are heavily dependent on regularization parameters for getting row–column clusters, and they impose unnecessary burden on users thus limiting their use in practice. We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster, finds stable biclusters, and results in interpretable row–column associations. Simulations and real data analyses show that integrative sparse singular value decomposition outperforms several other single- and multi-view biclustering methods and is able to detect meaningful biclusters. iSSVD is a user-friendly, computationally efficient algorithm that will be useful in many disease subtyping applications.

Original languageEnglish (US)
Pages (from-to)2201-2216
Number of pages16
JournalStatistical methods in medical research
Issue number11
StatePublished - Nov 2022

Bibliographical note

Funding Information:
The project described was supported by grant 5KL2TR002492-03 from the National Institutes of Health, 1R35GM142695-01 from the National Institute Of General Medical Sciences of the National Institutes of Health, and by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.

Publisher Copyright:
© The Author(s) 2022.


  • Multi-view biclustering
  • biclustering
  • co-clustering
  • integrative biclustering
  • multiomics
  • stability selection


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