Figure 2.

Benchmarking STRIDE’s performance using simulated data. (A) The cell-type-by-topic distribution estimated by STRIDE. The color represents the probability that one topic exists in one given cell type. (B) Validation of the trained topic model on the scRNA-seq used for training. The confusion matrix reflects the consistency between the prediction and the truth for the cell-type assignment of scRNA-seq data. The value represents the number of cells that belong to one cell type and are predicted to all different cell types. The color represents the proportion of cells belonging to the cell type on the y-axis and classified as the cell type on the x-axis. (C) Benchmark of STRIDE’s performance on different gene sets. The box plot reflects the distribution of Pearson’s correlation calculated between the predicted cell-type proportion and the ground truth for each spot. (D) Benchmark of STRIDE’s accuracy against different deconvolution methods. The box plot reflects the overall distribution of Pearson's correlation calculated in each spot for each method. (E) Benchmark of STRIDE’s sensitivity and specificity against different deconvolution methods. In each simulated location, the cell types were divided into two groups according to the presence (blue) and absence (pink), and RMSE was calculated within each group separately. The box plot reflects the distribution of RMSE in different methods. (F) Benchmark of the ability to distinguish diverse cell types across different deconvolution methods. Pearson’s correlation between the predicted proportions and the ground truth was calculated for each cell type. The black line in each column indicates the median of different cell types’ correlation for each method. (G) Benchmark of STRIDE’s robustness against different deconvolution methods on the simulated dataset with different sequencing depths.