【报告摘要】Population-level single-cell gene expression data captures the gene expressions of thousands of cells from each individual within a sizable cohort. This data enables the construction of cell-type- and individual-specific gene co-expression networks by estimating the covariance matrices. Understanding how such co-expression networks are associated with individual-level covariates is crucial. This paper considers Fréchet regression with the covariance matrix as the outcome and vector covariates, using the Wasserstein distance between covariance matrices as a substitute for the Euclidean distance. A test statistic is defined based on the Fréchet mean and covariate-weighted Fréchet mean. The asymptotic distribution of the test statistic is derived under the assumption of simultaneously diagonalizable covariance matrices. Results from an analysis of large-scale single-cell data reveal an association between the co-expression network of genes in the nutrient sensing pathway and age, indicating a perturbation in gene co-expression networks with aging. More general Fréchet regression on the Bures-Wasserstein manifold will also be discussed and applied to the same single-cell RNA-seq data.