Fig. 4

Summary of differential allele frequency analysis to detect coding mutations. A Summary of variation filtering and analysis pipeline implemented in FLAMES. Candidate variants are filtered based on allele frequency first, then based on per cell allele frequency to remove technical artifacts. The remaining variants are used for differential allele frequency analysis. B PCA on alternative allele matrix using the variants after filtering, colored by unsupervised clustering results using top PCs and annotated with cell lines. C Manhattan plot of P-values from a differential allele frequency analysis with Benjamini–Hochberg adjustment. Genes with significant variants are labeled. D UMAP visualization highlighting two CLL populations that have differential allele frequency for the significant variants. E UMAP visualization of cells colored by Gly101Val mutation status