Generalized integrative principal component analysis for multi-type data with block-wise missing structure

Huichen Zhu, Gen Li, Eric F. Lock

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)302-318
Number of pages17
JournalBiostatistics (Oxford, England)
Volume21
Issue number2
DOIs
StatePublished - Apr 1 2020

Bibliographical note

Publisher Copyright:
© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords

  • Block-wise missing imputation
  • Exponential family
  • Exponential principal component analysis
  • Joint and individual variation explained
  • Multi-view data

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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