Additional file 1 of SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models
Creators: name: Strober, Benjamin J.
name: Tayeb, Karl
name: Popp, Joshua
name: Qi, Guanghao
name: Gordon, M. Grace
name: Perez, Richard
name: Ye, Chun Jimmie
name: Battle, Alexis
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Supplementary methods. Fig. S1. Computational runtime of SURGE latent context inference and SURGE interaction eQTL calling. Fig. S2. Evaluation of SURGE’s ability to re-capture simulated latent contexts simulations. Fig. S3. Evaluation of SURGE’s ability to identify correct number of simulated latent contexts in simulations. Fig. S4. Proportion of expression variance explained by SURGE latent contexts when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S5. Q-Q plot for SURGE interaction eQTLs identified when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S6. Q-Q plot for SURGE interaction eQTLs relative to expression PC interaction eQTLs when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S7. Comparison of inferred SURGE latent contexts with and without random effects included when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S8. SURGE latent context 4 and SURGE latent context 7 are explained by genotype PC1 when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S9. Relationship between SURGE latent contexts and xCell cell type enrichment score when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S10. Relationship between SURGE latent context 5 and Epithelial cell type enrichment score, and SURGE latent context 6 and Neuron cell type enrichment score. Fig. S11. Correlation between SURGE latent contexts and gene expression principal components and genotype principal components when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Fig. S12. Relationship between SURGE latent context 1 and xCell cell type enrichment score when SURGE was applied to samples from Colon-Sigmoid GTEx v8 tissue. Fig. S13. P values of SURGE latent context 1 interaction eQTLs when SURGE was applied to samples from only Colon-Sigmoid GTEx v8 tissue compared to pvalues of interaction eQTLs using xCell cell type enrichment score. Fig. S14. Distribution of cells per pseudocell and distribution of pseudocells per individual. Fig. S15. Proportion of expression variance explained by SURGE latent contexts when SURGE was applied to PBMC pseudocells. Fig. S16. Q-Q plot for SURGE interaction eQTLs identified when SURGE was applied to PBMC pseudocells. Fig. S17. Q-Q plot for SURGE interaction eQTLs relative to expression PC interaction eQTLs when SURGE was applied to PBMC pseudocells. Fig. S18. SURGE latent contexts capture known cell type when SURGE was applied to PBMC pseudocells. Fig. S19. UMAP-projected SURGE latent context loadings of pseudocells colored by cell type labels when SURGE was applied to PBMC pseudocells. Fig. S20. UMAP-projected SURGE latent context loadings of pseudocells colored by cell type marker genes when SURGE was applied to PBMC pseudocells. Fig. S21. SURGE latent context 1 captures differences in disease status of individuals when SURGE was applied to PBMC pseudocells. Fig. S22. Correlation between SURGE latent contexts and gene expression principal components when SURGE was applied to PBMC pseudocells. Fig. S23. Variance explained of SURGE latent contexts by various pseudocell sample characteristics when SURGE was applied to PBMC pseudocells. Fig. S24. Number of colocalizations identified between 15 GWAS studies and various types of eQTLs called on PBMC pseudocells. Fig. S25. Number of trait colocalizations with SURGE interaction eQTLs stratified by SURGE latent context when SURGE was applied to PBMC pseudocells. Fig. S26. Complex tratit S-LDSC enrichment along eQTLs of SURGE latent contexts. Table S1. Number of genes with a genome-wide significant SURGE interaction eQTL according to per “per context empirical FDR” when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Table S2. Number of genes with a genome-wide significant SURGE interaction eQTL according to per “all context empirical FDR” when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Table S3. Association significance between tissue identity and SURGE latent context when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Table S4. Association significance between known ancestry and SURGE latent context when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Table S5. P-value of association between SURGE latent context and xCell cell type enrichment scores when SURGE was applied to samples concatenated across 10 GTEx v8 tissues. Table S6. Number of genes with a genome-wide significant SURGE interaction eQTL according to per “per context empirical FDR” when SURGE was applied to PBMC pseudocells. Table S7. Number of genes with a genome-wide significant SURGE interaction eQTL according to per “all context empirical FDR” when SURGE was applied to PBMC pseudocells. Table S8. Association significance between known cell type and SURGE latent context when SURGE was applied to PBMC pseudocells. Table S9. Association significance between known fine-grained cell type and SURGE latent context when SURGE was applied to PBMC pseudocells. Table S10. Hallmark gene set enrichment analysis of genes strongly correlated with SURGE latent contexts when SURGE was applied to PBMC pseudocells. Table S11. MSigDB Biological Process gene set enrichment analysis of genes strongly correlated with SURGE latent contexts when SURGE was applied to PBMC pseudocells.