The aim of data normalization in proteomics is to correct for the variability that is not coming from the biological system itself but from the experimental process, mainly multistep sample preparation, and LC-MS instrumentation, especially if the samples were processed over long period of time or measured using different instruments. This variance can cause a bias, affecting the biological conclusions.
Spectronaut default settings or BGS Factory Settings applies normalization to the data to minimize the effect of the variability generated by the sample preparation and the LC MS performance. This default normalization is based on the assumption that the samples used are similar, meaning the majority of the precursors within the samples are not regulated and that, for those which are, there is a similar number of peptides up and down regulated.
The default normalization is performed on the precursor level across all experimental samples (cross-run normalization). The normalized precursors quantities are subsequently used to derive peptides and protein groups quantities, without additional normalization at those levels.
By default, Spectronaut will choose between local and global normalization algorithms based on the number of runs in an experiment. If the number of experimental runs does not exceed 500 (n<500), local normalization will be used, and global normalization will be applied in larger experiments.
As usual, the default settings are suitable for most but not all experimental setups. The user can select local or global normalization independently of the experimental size. For most experiments both local and global normalizations perform in a similar manner. However, local normalization, due its more complex algorithm, might require higher computational power and time, especially in larger experiments. Some examples of experiments where local normalization could perform better are those where proteomes of multiple species are mixed and analyzed together, or in samples with very low complexity.
Normalization can also be turned off. This would be advisable, for example when analyzing samples with different levels of complexity.
You can change the default normalization strategy in three ways, as explained in the article "How do I change the default settings?" here. Briefly, before running the analysis, you can:
1. Create and save a custom schema with your suitable normalization option by going into the Settings Perspective (Figure 1), or
2. Change the option while setting up the experiment in the Experiment Setup window (Figure 2).
Figure 2. Changing the
analysis settings on the Experiment Setup window
Figure 3. Changing the settings to an already analyzed
To see the effect of the normalization, Spectronaut creates a pair of plots where you can visualize your quantitative data throughout your runs before and after normalization. You can find them in the Post Analysis Perspective, Analysis Overview node and Normalization (figure 4).
Figure 4. The Normalization node shows the effect of
normalization on your data. The left side shows boxplots of precursor responses
before normalization for each run. The right side shows boxplots of the same
precursor responses after normalization
One important aspect regarding normalization are those cases in which the samples are pre fractionated. In such cases, normalization should not be done throughout all runs, but by fraction. This should be annotated accordingly in the Conditions Editor when you set up your experiment (figure 5).
Figure 5. Annotating the fractions on the Conditions Editor is required for proper normalization
Callister SJ, Barry RC, Adkins JN, Johnson ET, Qian W, Webb-Robertson B-JM, Smith RD, and Lipton MS (2006) Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics. J Proteome Res 5:277–286.