Fig. 2

Excellent performance of Celloscope on simulated data. A Box plots represent distributions of the average absolute error (y-axis; computed using Eq. 16) for different methods (colors) and data simulation scenarios (x-axis). Celloscope outperforms competing methods, stereoscope [8], RCTD [7], and SpatialDWLS [13] which rely on additional input regarding gene expression levels in different cell types. Moreover, Celloscope is robust to noise in cell counting results and its performance remains satisfactory in case of no prior knowledge about the number of all cells in ST spots. B Distribution of the fraction of correctly identified dominant cell types across spots (y-axis), for different methods (colors) and simulation scenarios (x-axis). Celloscope again shows a large advantage over other methods. Here, we compare also to CellAssign [21], indicating the benefit from decomposing a mixture of different cell types in ST spots. C The impact of a lack of exclusivity in marker gene sets for cell types. Overlap in marker gene sets does not affect Celloscope’s performance in case of dense data and the performance remains satisfactory for sparse data. D BayesPrism [15] requires at least 50 genes per type to perform inference successfully