Figure 4. Predictions with reference profiles from tumor-infiltrating cells.

Same as Figure 3 but based on reference profiles built from the single-cell RNA-Seq data of primary tumor and non-lymphoid metastatic melanoma samples from Tirosh et al. (2016). (A) Comparison with flow cytometry data of lymph nodes from metastatic melanoma patients. (B) Comparison with IHC from colon cancer primary tumors (Becht et al., 2016). (C) Comparison with single-cell RNA-Seq data (Tirosh et al., 2016). For primary tumor and non-lymphoid metastasis samples, a leave-one-out procedure was used (see Materials and methods). Proportions of cells observed experimentally are given in Supplementary file 3A,E.

Figure 4—figure supplement 1. Comparison of EPIC results per cell type for gene expression reference profiles from circulating or tumor-infiltrating immune cells.

(A) Pearson R correlation and (B) RMSE between the cell fractions predicted and the experimentally measured fractions (from flow cytometry of lymph nodes from metastatic melanoma patients (this study), colorectal cancer IHC from primary tumors (Becht et al., 2016) and single-cell RNA-Seq data from melanoma (Tirosh et al., 2016). NA’s indicate cases where the cell type could not be predicted by a method. #: No predictions for endothelial cells were done in the primary tumors from single-cell RNA-seq data as only one patient had such cells and no profiles could be built through the leave-1-out procedure used for this dataset. The ‘Cancer +other cells’ correspond to cancer cells and other stromal and endothelial cells. No RMSE value can be computed for the IHC data in (B) as the measured values are not for all cells and do not reflect cell proportions. In (A) the significance of the Pearson correlation is indicated by stars: * p.value < 0.05, ** p.value < 0.01, *** p.value < 0.001, while results with p-values above 0.1 are inside parentheses.