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High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis

Publication ,  Journal Article
Shi, P; Zhou, Y; Zhang, AR
Published in: Biometrika
June 1, 2022

In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.

Duke Scholars

Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

June 1, 2022

Volume

109

Issue

2

Start / End Page

405 / 420

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics
 

Citation

APA
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ICMJE
MLA
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Shi, P., Zhou, Y., & Zhang, A. R. (2022). High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis. Biometrika, 109(2), 405–420. https://doi.org/10.1093/biomet/asab020
Shi, P., Y. Zhou, and A. R. Zhang. “High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis.” Biometrika 109, no. 2 (June 1, 2022): 405–20. https://doi.org/10.1093/biomet/asab020.
Shi, P., et al. “High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis.” Biometrika, vol. 109, no. 2, June 2022, pp. 405–20. Scopus, doi:10.1093/biomet/asab020.
Journal cover image

Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

June 1, 2022

Volume

109

Issue

2

Start / End Page

405 / 420

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics