Fig. 2: The performance evaluation results of different methods on a semi-simulation experiment using mouse embryos.
From: STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning
a An illustration of pseudo-ST data generation. Spots on ST data contain only a fraction of single cells. b The reconstructed spatial distribution by STEM versus the ground truth spatial distribution. Colors indicate different cell types or regions. c The mean absolute error (MAE), hit number and Pearson correlation coefficient (PCC) performance of different methods on the first mouse embryo data. The lower the MAE, the better. The higher the hit number and PCC, the better. In PCC results, the two edges of box and horizontal bar inside the box represent the interquartile and median of all values, respectively. d The PCC and MSE performance of methods on five cell types. These manually selected cell types covered all comparison results (equal, lower and higher) between the PCC of Tangram and STEM. The bar plot shows the PCC between ground truth and predicted spatial distributions of five cell types. In MAE results, we used an enhanced boxplot to show more quintiles. The horizontal bar inside the box represents the median of all values. Each edge of the box represents the half percentiles of the rest data, in other words, splitting the rest data into two halves.
