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Fig. 5 | Genome Biology

Fig. 5

From: BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

Fig. 5

The robustness of BayesCCE to prior misspecification and its ability to capture population-specific variability in cell-type composition, under the assumption of six constituting cell types in blood (k=6): granulocytes, monocytes, and four subtypes of lymphocytes (CD4+, CD8+, B cells, and NK cells). Left side: t test results (presented by the negative log of the Bonferroni-adjusted p values) for the difference in proportions of each cell type between cases and controls. Right side: the Dirichlet parameters of estimated cell counts stratified by cases and controls; red dashed rectangles emphasize the high similarity in the estimated case/control-specific cell composition distributions yielded by the different methods, regardless of the prior used (“prior”). Results are presented for four different data sets and using cell count estimates obtained by four approaches: the reference-based method, BayesCCE, BayesCCE with known cell counts for 5% of the samples (BayesCCE imp), and BayesCCE with 5% additional samples with both known cell counts and methylation from external data (BayesCCE imp ext). For the Hannum et al. data set, for the purpose of presentation, cases were defined as individuals with age above the median age in the study. In the evaluation of BayesCCE imp and BayesCCE imp ext, samples with assumed known cell counts were excluded before calculating p values and fitting the Dirichlet parameters

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