For both data sets, MS 2 PIP and DeepLC exhibit overall good performance. The MS²PIP model shows a slightly lower-than-expected Pearson correlation coefficient of 0.86 between observed and predicted fragment ion intensities. For DeepLC, the performance of the model used for rescoring of the single-cell data is comparable to the performance of 460 benchmark data sets DeepLC has been evaluated on (percentile 43.0% for the 500ms data set and percentile 44.1% for the 750ms data set in terms of relative mean absolute error (see HTML QC reports in Supplementary Material under "Rescoring features")). The HTML QC reports also show that features from MS 2 PIP are contributing the most to Mokapot's discrimination of target and decoy hits across both data sets. Additionally, the binomial score and weighted probability from the MS Amanda output file (psm_file in the reports), the MS Amanda score (basic feature generator in the reports), alongside std_abs_diff_norm and max_abs_diff_norm, collectively emerge as the individual features that contribute the most in effectively distinguishing target and decoy PSMs.