Fig. 1: Principal components of sc-eQTLs separate biologically distinct cell types.
We calculate the principal components (PCs) of significant sc-eQTLs in the meta-analysis of datasets from Fujita et al. 21 and Bryois et al. 20. Each data point in the plot represents a distinct cell type. Biologically similar cell types tend to be clustered together in the plot, e.g., neurons (e.g., inhibitory and excitatory neurons), and glial cells (e.g., oligodendrocytes, astrocytes, and microglia). Using PCs as covariates, BASIC will borrow strength from biologically similar cell types and improve the power of identifying regulatory variants. As we show in Supplementary Notes Section 2, the PCs of the sc-eQTLs effects are concordant with the PCs of the covariance matrix of cell type gene expression levels.
