ArticleBiostatistics (Oxford, England)2020
Generalized integrative principal component analysis for multi-type data with block-wise missing structure.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies.Mathematics (Basel, Switzerland) · 2024Article
- Multilayer Exponential Family Factor models for integrative analysis and learning disease progression.Biostatistics (Oxford, England) · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- sJIVE: Supervised Joint and Individual Variation Explained.Computational statistics & data analysis · 2022Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.BMC bioinformatics · 2022Article
- BIDIMENSIONAL LINKED MATRIX FACTORIZATION FOR PAN-OMICS PAN-CANCER ANALYSIS.The annals of applied statistics · 2022Article
- Generalized Co-Clustering Analysis via Regularized Alternating Least Squares.Computational statistics & data analysis · 2020Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Integrative factorization of bidimensionally linked matrices.Biometrics · 2020Article
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3 authors.
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Abstract
High-dimensional multi-source data are encountered in many fields. Despite recent developments on the integrative dimension reduction of such data, most existing methods cannot easily accommodate data of multiple types (e.g. binary or count-valued). Moreover, multi-source data often have block-wise missing structure, i.e. data in one or more sources may be completely unobserved for a sample. The heterogeneous data types and presence of block-wise missing data pose significant challenges to the integration of multi-source data and further statistical analyses. In this article, we develop a low-rank method, called generalized integrative principal component analysis (GIPCA), for the simultaneous dimension reduction and imputation of multi-source block-wise missing data, where different sources may have different data types. We also devise an adapted Bayesian information criterion (BIC) criterion for rank estimation. Comprehensive simulation studies demonstrate the efficacy of the proposed method in terms of rank estimation, signal recovery, and missing data imputation. We apply GIPCA to a mortality study. We achieve accurate block-wise missing data imputation and identify intriguing latent mortality rate patterns with sociological relevance.
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