Quantitative proteomics data quality and statistical power for discerning treatment differences
To demonstrate the power of using DIA assay libraries for accurate protein quantitation on a proteome-wide scale we analyzed the DIA data for six replicates of kidneys from fish acclimated to brackish water (BW) and freshwater (FW), each. A volcano plot was generated with Skyline 20.0 as previously described (Li, Levitan, Gomez-Jimenez, & Kültz, 2018) to visualize statistically significant proteins associated with acclimation of fish to BW (Figure 2A). QC charts of the DIA data illustrate that the majority of transitions had a mass error of less than 10 ppm (Figure 2B) and there was a perfect correlation of measured retention time (RT) with the predicted RT based on intrinsic RT (iRT) standards (Escher et al., 2012) with no outliers (Figure 2C). In addition to an adjusted p value < 0.05, which took into account multiple testing (Benjamini & Hochberg, 1995), a fold-change (FC) threshold was enforced in the volcano plots. The FC threshold was 1.85 as calculated by MSstats (Choi et al., 2014) using a statistical power analysis based on the coefficient of variance (CV) of all experimental DIA data (Figure 2D). Furthermore, the vast majority of transition peaks for all 12 samples in this dataset had mProphet (Reiter et al., 2011) peak scores of q < 0.01, which was the peak quality threshold for inclusion in MSstats quantitative DIA data analysis (Figure 2e). In this example, 21 kidney proteins were up-regulated and 21 down-regulated during acclimation of fish from FW to BW (Figure 2A).