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Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data

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  • Chris McKennan
  • Dan Nicolae

Abstract

Many high-dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput “omic” data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for, can severely obfuscate estimation of and inference on the effects of interest. We therefore developed CBCV and CorrConf: provably accurate and computationally efficient methods to choose the number of and estimate latent confounding factors present in high-dimensional data with correlated or nonexchangeable residuals. We demonstrate each method’s superior performance compared to other state of the art methods by analyzing simulated multi-tissue gene expression data and identifying sex-associated DNA methylation sites in a real, longitudinal twin study. Supplementary materials for this article are available online.

Suggested Citation

  • Chris McKennan & Dan Nicolae, 2022. "Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 225-236, January.
    • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:537:p:225-236
    DOI: 10.1080/01621459.2020.1769635
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    1. Brooke Szczesny & Meher Preethi Boorgula & Sameer Chavan & Monica Campbell & Randi K. Johnson & Kai Kammers & Emma E. Thompson & Madison S. Cox & Gautam Shankar & Corey Cox & Andréanne Morin & Wendy L, 2024. "Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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