Fig. 6From: SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomicsBenchmarking spatial expression analysis methods using synthetic data from SRTsim. Synthetic data are either generated based on the DLPFC reference (A–D) or the STARmap reference (E–H). A A representative gene in the DLPFC reference data (sample 151673) displays a random spatial expression pattern. B Representative genes in the DLPFC reference data display four distinct spatial expression patterns corresponding to four different layered structures (Layer1, Layer 2, Layer 3, and WM, respectively). C Quantile–quantile plot of the observed −log10(P) from different methods against the expected −log10(P) under the null SRTsim simulations based on the DLPFC reference. P values were combined across ten simulation replicates. D Power plots show the proportion of true positives (y-axis) detected by different methods at a range of FDRs (x-axis) for the alternative SRT simulations based on the DLPFC reference. Each panel corresponds to the spatial expression pattern displayed in B. SE strength is set to be five-fold. E A representative gene in the STARmap reference data displays a random spatial expression pattern. F Representative genes in the STARmap reference data display four distinct spatial expression patterns that correspond to four different layered structures (Layer2/3, Layer 4, Layer 5, and Layer 6, respectively). G Quantile–quantile plot of the observed −log10(P) from different methods against the expected −log10(P) under the null SRTsim simulations based on the STARmap reference. P values were combined across ten simulation replicates. H Power plots show the proportion of true positives (y-axis) detected by different methods at a range of FDRs (x-axis) for the alternative SRT simulations based on the STARmap reference. Each panel corresponds to the spatial expression pattern displayed in F. SE strength is set to be three-foldBack to article page