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.