Sensitivity of the different KNN to variability. (A) Heatmaps showing the sensitivity of the different KNNs to variability. The left heatmap indicates the position of the simulated differential interactions. The heatmaps labeled from k = 1 to k = 9 show the positions of the pairwise interactions showing a significant difference from their kth-nearest neighbor (P-value <0.001). The right heatmap shows the majority vote heatmap of the KNNs. We noticed that the furthest neighbors give noisier predictions. (B) The distribution of the P-values of the KNN heatmaps calculated in A. Plots 1–9 are the P-values obtained using μ1 as reference, and plots 10–18 are the P-values obtained using μ2 as reference. (C) Plot showing the convergence of the Fisher distribution using k as the degrees of freedom (k ∈ [1, 9]). We noticed that the larger the k is, the faster the Fisher distribution converges out of the acceptance zone.
