Fig. 2: Performance of different spot localization tools.

a, Detection accuracy for different tools was analyzed using the F₁ score calculated from true-positive (red circle, white spot), false-positive (red circle, no spot), and false-negative (no circle, white spot) detection. Corresponding false-positive and false-negative values can be found in Supplementary Figure SN4.2, in which data are represented as a boxplot with the full outlier range. F₁ score and localization error were determined using a set of 50 simulated images (256 × 256 × 32 pixels), with different noise levels (example images in Supplementary Fig. 4.1) containing either 30 spots (n = 39) or 300 spots (n = 11). The best detection parameters for each tool were determined by a grid search over the parameter space (details in Supplementary Notes). b, Localization error was measured as Euclidean distance (pixels) between the detected spot center and the ground-truth center of simulated spots for the same set of images described in a. c, Histograms of distance deltas of the ground truth to its corresponding localized spot separated by image dimensions (x,y,z) for the different tools, showing that all methods are highly accurate while precision varies. The corresponding localization error for each dimension separately can be found in Supplementary Fig. SN4.2d,e. d, Comparison of processing speed for 13 real 3D smFISH images of C. elegans embryos, with images sized around 30 MB containing an average of ~350 spots per image (example images in Supplementary Fig. SN4.1). Bar plots in a, b, and d, as well as the line plots in e and f, show the mean and a 95% confidence interval of the 50 measured detections. e, Influence of different image noise levels on spot detection. Plot displays detection accuracy measured as F₁ score (y axis) against the s.d. of image noise (x axis). Example images corresponding to the different noise levels are displayed below the graph. f, Influence of image noise on the localization error measured in Euclidean distance to the center of simulated points (ground truth) against the s.d. of image noise, using the same data as shown in b. For a–f, details on run parameters and tables with raw values are in Supplementary Notes.