Figure 6

Predictive performance and pairwise correlation of simulated AAWS decrease both with introduction of multiplicative Gaussian noise and antibody dominance. (A) Predictive performance (Q²) and (B) pairwise correlation of AAWS (r) for different simulated cases. (I) For a given peptide library and given assigned AAWS ($\overrightarrow{h}$) we simulated 100 realizations of binding profiles for a mixture of 16000 different antibodies. (II) Same as in I, but Gaussian multiplicative noise was introduced into the simulated signal intensities. (III) Same as in II, but the concentration of a single antibody (dominant antibody) was increased 10-fold. (IV) Same as in II, but concentration of one (dominant) antibody was increased 1000-fold. For both (A) and (B) a simulated peptide library (X_sim) and assigned AAWS ($\overrightarrow{h}$) were generated once and kept constant across the entire simulation. Simulated antibody binding profiles (${\overrightarrow{s}}_{\mathsf{\text{sim}}}^i$) were computed using Equation 2. Corresponding AAWS (${\overrightarrow{w}}_{\mathsf{\text{sim}}}^j$) were computed using Equation 1. In each of the 100 runs, a newly generated random antibody mixture of n_Ab = 15999 different antibodies was simulated to which the dominant antibody was added. This antibody was randomly generated once at the beginning of the simulation and was kept constant across all four simulation cases. Gaussian noise term: $\mathcal{N}$(μ = 0, σ = 0.01).