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Title: High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis
Summary 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.  more » « less
Award ID(s):
1944904
NSF-PAR ID:
10329216
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Biometrika
Volume:
109
Issue:
2
ISSN:
0006-3444
Page Range / eLocation ID:
405 to 420
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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