Structural zeros in high-dimensional data with applications to microbiome studies. (8th January 2017)

• Record Type: Journal Article
• Authors: Kaul, Abhishek; Davidov, Ori; Peddada, Shyamal D.
• Is Part Of: Biostatistics. Volume 18:Number 3(2017)
• Page Start: 422
• Page End: 433
• Publication Date: 2017-01-08
• Subjects: Classification -- High dimension -- Microbiome data -- Missing data -- Sparsity; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxw053
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: This paper is motivated by the recent interest in the analysis of high-dimensional microbiome data. A key feature of these data is the presence of "structural zeros" which are microbes missing from an observation vector due to an underlying biological process and not due to error in measurement. Typical notions of missingness are unable to model these structural zeros. We define a general framework which allows for structural zeros in the model and propose methods of estimating sparse high-dimensional covariance and precision matrices under this setup. We establish error bounds in the spectral and Frobenius norms for the proposed estimators and empirically verify them with a simulation study. The proposed methodology is illustrated by applying it to the global gut microbiome data of Yatsunenko and others (2012 . Human gut microbiome viewed across age and geography. Nature 486, 222–227). Using our methodology we classify subjects according to the geographical location on the basis of their gut microbiome.