Figure 1.

(a) A set of calibration curves for 3 transitions of a well-behaved peptide, with a relatively low LOD and a linear response region spanning three orders of magnitude (n = 4 for each transition at all concentration points). The left panel shows the data points on the linear scale along with the calibration curves. The panel on the right shows the data on a logarithmic scale so that all points are clearly visible, along with the calculated LOD. (b) A set of calibration curves for 3 transitions of a poorly behaved peptide with significantly inconsistent measurements, resulting in a high LOD, and a very restricted linear response region. (c) Regression lines fitted using ordinary least squares (OLS), weighted least squares (WLS) where each point is weighted by the inverse square of its theoretical concentration, robust regression (using the MM-estimator) and weighted robust regression (MM-estimator with inverse square weighting). Weighted regression lines for least squares regression and robust regression are almost identical, with the robust regression line coming close. OLS is most affected by a few outliers. (d) Example calibration curves (i) site 19 transition 37tr1_A in blue on the top, and (ii) site 56 transition 167tr3_A in green (bottom), that have ideal slopes (i.e., slope = 1, see Table 1 and Section 3) when the regression line is fit using log-transformed data, but clearly have slope > 1 in linear space. The black diagonal line represents slope = 1 in the panels above.

Mani et al. BMC Bioinformatics 2012 13(Suppl 16):S9   doi:10.1186/1471-2105-13-S16-S9