Fig 1.
Summary of the experiments. Starting from the original segmentation mask, the perturbation module generates a perturbed mask. For both masks, feature tables are generated. Based on these tables, further analysis and the corresponding results are compared.
Fig 2.
Quality control of perturbations.
(A) Comparison of the original (white) and perturbed cell masks (colored according to perturbation strength: blue, red, green, yellow) based on mask borders on a randomly selected image slide. (B) Kernel-density estimation (KDE) for single-cell expression residuals between original and perturbed data. (C) KDE for median area changes of cells. (D) KDE for the mean absolute error between true and perturbed feature correlation matrices. (E) KDE for the results of the classifier 2-sample test.
Fig 3.
(A) Jaccard similarity of kNN sets (JKNN) for cells in different perturbation levels. (B) Aggregation of results for different perturbation strengths. (C) Aggregation of results for different numbers of neighbors. Error bars indicate one median absolute deviation.
Fig 4.
Effect of segmentation error on k-Means clustering.
(A) Adjusted Rand Index (ARI) scores for different numbers of clusters. (B) Aggregations of ARI scores for perturbation levels. (C) ARI scores for different numbers of clusters for variance stabilized data. (B) Aggregations of ARI scores for perturbation levels for variance stabilized data. (D) ARI scores are aggregated for the number of clusters. Error bars indicate one median absolute deviation. Colors encode either perturbation strength or the applied preprocessing.
Fig 5.
Effect of segmentation error on Leiden clustering.
(A) Adjusted Rand Index (ARI) scores for different sizes of neighborhoods in the kNN-graph and perturbation strengths. (B) Aggregations of ARI scores for perturbation levels. (C) ARI scores for different sizes of neighborhoods in the kNN-graph and perturbation strengths for variance stabilized data. (D) ARI scores are aggregated for the number of neighbors. (E) Histogram showing the relationship between neighborhood size and resulting number of clusters. Error bars indicate one median absolute deviation. Colors encode either perturbation strength or the applied preprocessing.
Fig 6.
Effect of segmentation errors on phenotype assignments.
(A) Balanced accuracy between ground truth and perturbed data. (B) Confusion matrix showing the errors for the Gaussian mixture model (GMM) based phenotyping. The shown perturbation is according to state of the art (F1=90). Only values are shown. Color encodes the percentage of classified cells. For visual appearance, only off-diagonal elements are colored in. (C) Wu and Palmer distance for F1=90 based on the used gating strategy. Error bars indicate one median absolute deviation.
Fig 7.
Results of GMM-based phenotyping
(A) Median balanced accuracy when comparing phenotypes of ground truth and perturbed cells. (B) kNN accuracies aggregated over perturbation strengths. (C) kNN accuracies aggregated over neighborhood sizes.