## 3 Results  
First, we determined the influence of different parameter settings on the experimental results, as shown in Figure 4.  
The ordinate means the number of identified peptides. We chose 1000 MS/MS spectra for testing. Considering the cost and experimental efficiency, the following thresholds led to the most identification number results when using MSFragger searches for pseudo-MS/MS spectra extracted in different settings (at 1% filter) and were selected as default values in our algorithm: the cosine similarity was set to a threshold of 0.6, the top 30 ranked precursors for each MS/MS spectrum were used, and we chose a 1-min retention time interval. Next, we extracted ion chromatograms (XICs) for all fragment and candidate precursor ions in the isolation window. According to the consistency between the precursor and its fragment ion chromatograms, the entire MS/MS spectrum was interpreted, and the correspondence between precursor and fragment ions was found. The charge state of the precursor was also determined. In this way, we could obtain the pseudo-MS/MS spectra. In Figure 5, we show the chromatograms of the precursors and their fragment ions with high similarity. The three curves in the upper part of Figure 5 are precursor ion XICs, and the curves in the lower part of Figure 5 are their corresponding fragment ion XICs. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Precursor tolerance & \(\pm\)20 ppm \\ Fragment tolerance & \(\pm\)20 ppm \\ Max variable mods & 3 \\ Peptide length & 5–50 \\ Peptide mass range & 100–5000 \\ Filter & 1\% \\ \hline \end{tabular}
\end{table} 
Table 2: Database search parameters of MSFragger. Figure 4: Influence of different parameter settings on experimental results. Then, we tested the effect of removing the isotopic peak cluster in the process of interpreting the spectrum. In the Materials and Methods section, we mentioned that for each MS/MS, we chose the top 30 precursor ions. Because the chromatograms of the precursor and its isotopic peaks are consistent, we took the top 30 precursor ions and their identification results before and after removing the isotopic peak cluster; an example is shown in Figure 6. The table on the left represents the top 30 precursor ions selected before removing the isotopic peak cluster and their identification result sequences. The identification results of the blue and green isotope peak clusters are completely consistent, and there is redundancy, which is not conducive to improving the result performance. The right side shows the top 30 precursor ions that were selected after removing the isotopic peak cluster and their identification result sequences, and the number of identification results significantly increased. Figure 5: The XIC consistency of precursor with its fragment ions. Figure 6: The XICs of the top 30 precursors and identification sequences before and after removing the isotopic peak cluster. In previous works, there was one traditional, main pseudo-MS/MS spectra method, known as DIA-Umpire. We compared the performance of CorrDIA and DIA-Umpire for analyzing the HeLa or Human dataset. The output.mgf file format was found to be consistent with the conventional DDA.mgf files; thus, any DDA database search software or de novo software can be used for the identification of peptides and proteins. In this work, we used the database-searching software MSFragger to identify peptides and saved the result mass spectra files under the filter (1%) controlled. First, we compared the number of different identified peptide ions of CorrDIA and DIA-Umpire. In Figure 7a (the numbers in the figure are the numbers of identified peptides), about 63% of the peptides identified by CorrDIA were also identified by DIA-Umpire, and about 71% of the peptides identified by DIA-Umpire were also identified by CorrDIA, a 12% increase compared to DIA-Umpire.