High-dimensional compositional data arise naturally in many applications such as metagenomic data analysis.The observed data lie in a high-dimensional simplex,and conventional statistical methods often fail to produce sensible results due to the unit-sum constraint.In this article,we address the problem of covariance estimation for high-dimensional compositional data,and introduce a composition-adjusted thresholding(COAT)method under the assumption that the basis covariance matrix is sparse.Our method is based on a decomposition relating the compositional covariance to the basis covariance,which is approximately identifiable as the dimensionality tends to infinity.