There is a great interest in reliable ways to obtain absolute protein abundances 
at a proteome-wide scale. To this end, label-free LC-MS/MS quantification 
methods have been proposed where all identified proteins are assigned an 
estimated abundance. Several variants of this quantification approach have been 
presented, based on either the number of spectral counts per protein or MS1 peak 
intensities. Equipped with several datasets representing real biological 
environments, containing a high number of accurately quantified reference 
proteins, we evaluate five popular low-cost and easily implemented 
quantification methods (Absolute Protein Expression, Exponentially Modified 
Protein Abundance Index, Intensity-Based Absolute Quantification Index, Top3, 
and MeanInt). Our results demonstrate considerably improved abundance estimates 
upon implementing accurately quantified reference proteins; that is, using 
spiked in stable isotope labeled standard peptides or a standard protein mix, to 
generate a properly calibrated quantification model. We show that only the Top3 
method is directly proportional to protein abundance over the full 
quantification range and is the preferred method in the absence of reference 
protein measurements. Additionally, we demonstrate that spectral count based 
quantification methods are associated with higher errors than MS1 peak intensity 
based methods. Furthermore, we investigate the impact of miscleaved, modified, 
and shared peptides as well as protein size and the number of employed reference 
proteins on quantification accuracy.