Fig. 2

CluBCpG identifies unique read clusters that associate with cell type. a Schematic depicting how data were iteratively divided into random splits to perform cell type comparisons using CluBCpG. b, c Bar graphs representing the average proportion of clusters unique to either input across 10 rounds of random sampling; comparisons were performed for b human B cells and monocytes and c human neurons and glia. Error bars represent the standard deviation from the mean; statistical test: one-way ANOVA, f-statistics are 83,978 (b) and 6725 (c), 2 degrees of freedom. In both cases, > 20-fold more unique clusters were identified when different cell types are compared. d, e Venn diagrams of all genomic bins with a cell type-specific cluster identified in d the full data set B cell vs. monocyte comparison and e the neuron vs. glia comparison. f In the B cell vs. monocyte comparison, < 10% of bins with a cell type-specific cluster overlap with a B cell vs. monocyte DMR. g Histogram showing the proportional representation of sample reads per B cell-specific cluster in the B cell vs. monocyte comparison. Clusters comprising ≥ 50% or < 50% of the B cell reads in that bin are termed “major” and “minor” clusters, respectively. Inset illustrates the concept. h, i Heatmaps showing the top 10 GO biological process terms associated with bins containing h a B cell- or monocyte-specific cluster or i a neuron- or glia-specific cluster. j Heatmap of the top 10 GO biological process terms from B cell and monocyte bins containing a major cluster. Colors in all heatmaps represent the -log10 of the q value calculated by GREAT