the species of the reference samples (Additional file 1: Figure S1b; Additional file 1: Table S2). We observed that some samples did not cluster with their expected cell types and coincidently the LOOCV indicated that these samples had an expression signatures that differed from the other samples of the same cell type. However, we found that all of these outliers correspond to samples not correctly purified or that were sequenced in early stages of differentiation (Additional file 1: Supplementary Results). After discarding these samples, we assessed six digital deconvolution algorithms implemented in the CellMix package [62] and found that the ssNMF [57] calculated the most accurate estimates (see “Methods”). Our final reference panel (Additional file 1: Table S2; Additional file 1: Table S3) had a very high confidence to predict cell types with a mean predicted accuracy = 95.2%, s.d. = 4.3 (Additional file 1: Figure S2b), and a RMSE = 0.06 (Additional file 1: Table S5).
