Benchmarking Quantitative Performance in Label-Free Proteomics

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini–Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.