Pseudocode for generation of simulated datasets

Input:- n_exp: number of experiments- n_prot_mean: mean number of proteins present per experiment- n_prot_stdev: stdev number of proteins present per experiment- tp_score_mean: mean of score distribution for true positives- tp_score_stdev: stdev of score distribution for true positives- fp_score_mean: mean of score distribution for false positives- fp_score_stdev: stdev of score distribution for false positives- incorrect_ratio: proportion of incorrect peptides without FDR threshold- peptide_fdr: peptide fdr threshold, should be lower than protein_fdr- protein_probs: probability for each protein to be present- peptide_probs: probability for each peptide (including shared peptides!) to be present given that the protein is present Algorithm: 1. For each experiment a. Pick n_prot ∼ N(n_prot_mean, n_prot_stdev) proteins to be present b. Draw n_prot true positive target proteins from the target database c. Calculate minimum peptide score corresponding to given peptFDR and incorrect_ratio: min_score = Φ-1(1 - peptFDR ∗ (1 - incorrect_ratio) / incorrect_ratio; fp_score_mean, fp_score_stdev) d. For each true positive target protein i. Draw true positive peptides based on their peptide_probs ii. Draw score for each true positive peptide from a truncated normal distribution: trunc_norm(min_score, ∞; tp_score_mean, tp_score_stdev) e. Randomly draw 2 ∗ tp_peptides ∗ peptide_fdr / (1 - peptFDR) false positve peptides from all peptides in target and decoy databases f. Draw score for each false positive peptide from a truncated normal distribution: trunc_norm(min_score, ∞; fp_score_mean, fp_score_stdev) 2.Do protein grouping based on observed peptides 3.Calculate protein group FDRs