Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc
Fig 5
Identification of GWAS trait-associated cell types using PaaSc.
(A) Enrichment of lymphocyte count-associated genes across 19 sorted cell types. A box plot showing the enrichment scores of lymphocyte count-associated genes calculated by PaaSc. Cell types are categorized into blood, brain, or other categories. (B) Distribution of PaaSc scores for lymphocyte count-associated genes. A histogram illustrating the bimodal distribution of PaaSc scores, enabling the classification of cells into positive and negative states on the basis of the enrichment of lymphocyte count-associated genes. (C) Significance of enrichment of lymphocyte count-associated genes across 19 sorted cell types. Positive cells were defined using the cutoff established in (B). A one-sided Fisher’s exact test was performed, and the log10-transformed odds ratio and the negative log10-transformed false discovery rate (–log10 FDR) are shown. (D) Comparison of PaaSc, CelliD, and GSDensity in identifying GWAS trait-associated cell types. Positive cells were defined as the top 5% of cells with the highest scores for each method. Statistical significance was assessed using a one-sided Fisher’s exact test, with significant results marked by an asterisk (*).