Fig. 2

Evaluation of the performance of xCell using simulated mixtures. a An overview of adjusted scores for 43 cell types in 259 purified cell type samples from the Blueprint and ENCODE data sources (other data sources are in Additional file 2: Figure S4). Most signatures clearly distinguish the corresponding cell type from all other cell types. b A simulation analysis using GSE60424 as the data source [26], which was not used in the development of xCell. This data source contains 114 RNA-seq samples from six major immune cell types. Left: Pearson correlation coefficients using our method before spillover adjustment and after the adjustment. Dependencies between CD4+ T cells, CD8+ T cells, and NK cells were greatly reduced; spillover from monocytes to neutrophils was also removed. Right: Comparison of the correlation coefficients across the different methods. The first column corresponds to xCell’s predictions of the underlying abundances of the cell types in the simulations (both color and pie chart correspond to average Pearson coefficients). Bindea, Charoentong, Palmer, Rooney, and Tirosh represent sets of signatures for cell types from the corresponding manuscripts. Newman refers to the inferences produced using CIBERSORT on the simulations. xCell outperformed the other methods in 17 of 18 comparisons. c Comparison of the correlation coefficients across the different methods based on 18 simulations generated using the left-out testing samples. Here rows correspond to methods and columns show the average Pearson coefficient for the corresponding cell type across the simulations. Independent simulations are available in Additional file 2: Figure S6. xCell outperformed the other methods in 64 of 67 comparisons