Fig. 3. Gene expression similarity between datasets predicts eQTL similarity.

a, MDS analysis of median gene expression across datasets. The pairwise similarity between datasets was calculated using Pearson’s correlation. Datasets from GTEx and BLUEPRINT studies have been highlighted to demonstrate that they cluster with other matching cell types and tissues. b, MDS analysis of eQTL sharing across datasets. Pairwise eQTL sharing between datasets was estimated using the Mash model. The complete matrix is presented in Extended Data Fig. 2. c, Visualization of eQTL-sharing estimates between selected representative tissues (x axis) and all other cell types and tissues in the eQTL Catalogue. The individual points have been colored according to the major cell type and tissue groups from a. d, Matrix factorization of the eQTL effect sizes across all eQTL Catalogue datasets. The heatmap represents the loadings of 21 latent factors in each of the 86 naive datasets. Nine datasets from stimulated macrophages and monocytes have been excluded to improve legibility. The version of this heatmap with dataset labels is shown in Extended Data Fig. 7.