Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Abstract

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.

Figure 1

Comparison of triplicate sets of relative peptide abundances in the absence of biological variability prior to and following normalization with the local regression technique. Scatter plots represent peptide ratios (ordinate) versus their mean abundances (abscissa) for (a) 111 common peptides from the standard proteins sample, block 3, (b) 1032 common peptides from the stationary phase growth sample of D. radiodurans, block 2, and (c) 1605 common peptides from the methamphetamine induced mouse striata tissue sample, block 3. Solid lines represent potential non-linearly dependent systematic bias estimated from application of the locally weighted regression and smoothing scatter plots (LOWESS) function.

Figure 2

Comparison of summed ranks for central tendency, linear regression, local regression, and quantile normalization approaches applied to LC-FTICR MS runs without biological variability. (a) Ranks by percent reduction in extraneous variability estimated as PEV. Note that the lower the value of the summed rank, the greater the percent reduction in this estimate of extraneous variability. Linear regression and quantile normalization performed similarly and received better rankings than central tendency and local regression normalization. (b) Ranks by percent reduction in extraneous variability estimated by the median CV. Here, central tendency and linear regression normalization performed similarly, and better than local regression and quantile normalization.

Figure 3

Quantile plots with box plots (inset) comparing elements (replicates) of relative peptide abundances for early-log phase growth of Deinococcus radiodurans relative to stationary phase growth. (a) Prior to normalization. (b) Following quantile normalization. (c) Following central tendency normalization. While quantile normalization resulted in the largest percent reduction in extraneous variability estimated using PEV, it ranked behind central tendency normalization in terms of extraneous variability estimated using the median CV of peptides common to all elements.