Analysis of High Dimensional Compositional Data Containing Structural Zeros with Applications to Microbiome Data

This paper is motivated by the recent interest in the analysis of high dimensional microbiome data. A key feature of this 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 insufficient 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 support them with a simulation study. We also apply the proposed methodology to the global human gut microbiome data of Yatsunenko (2012).