Fig. 7: Deconvolution of immune cell types from WGBS data.
From: Benchmarking of methods for DNA methylome deconvolution
a Matrix of marker regions (n = 600) used for building immune cell methylation reference of WGBS data. Samples for six cell types were included: neutrophil (n = 3), natural killer cell (n = 3), B-cell (n = 5), CD4+ T-cell (n = 10), CD8+ T-cell (n = 10), and monocyte (n = 3). b Boxplots showing accuracy scores for 16 different deconvolution methods, seven normalization methods, and six cell types on 100 in silico mixtures. Black diamond shapes: median values, colors: cell types. The boxplots present median values and quartiles, whiskers the minimum and maximum values, and dots the individual data points. P-values were determined using two-tailed FDR-adjusted Dunn’s tests. *P < 0.05, **P < 0.01, ***P < 0.001. c Performance of deconvolution on 100 in silico mixtures. Algorithm-normalization combinations are visualized as circles. Spearman’s R2 is represented by color, root mean squared error is represented by size. Rows show deconvolution algorithms, columns show normalization methods. d Scatter plots showing true (x-axis) and predicted proportions (y-axis) for the best (left-upper) and worst-performing (right-lower) algorithm-normalization combinations on 100 in silico mixtures. R2 and p-values were calculated using Spearman’s rank correlation test. ‘X’ symbol represents missing values. Source data are provided as a Source Data file. Exact p-values are added in the Source Data file.
