Table 1 

Comparison of the regression analysis in linear and log space. 

Regression slope after logtransformation 

Regression slope in linear space 
not ideal 
ideal (slope = 1) 


not ideal 
167 
53 
ideal (slope = 1) 
14 
6 


Measured and theoretical concentrations from NBT Study I (with 8 sites, 10 peptides and 3 transitions per peptide) are natural log transformed (other common bases used for the log transform are 2 and 10). Weighted, robust regression lines are fitted to the linear data while robust regression is used for logtransformed data. The slope of each regression is assumed to be 1 (ideal) if the 95% confidence interval calculated as slope ± t_{(1Δ/2),df }* s.e. includes 1 (where, t_{(1Δ/2),df } is the 2tailed tdistribution critical value for α = 0.05, df = (# replicates  1), and s.e. is the standard error for the regression slope). As is evident from the table, logtransforming data results in more ideal slopes (closer to 1). Visual inspection of the regression lines and associated data indicate that this overestimation of the number of ideal peptides by logtransformed regression is unwarranted and misleading, as illustrated by examples in Figure 1d. 

Mani et al. BMC Bioinformatics 2012 13(Suppl 16):S9 doi:10.1186/1471210513S16S9 