Data normalization in high throughput experiments is usually performed to correct for the variability that is not coming from the biological system itself but from the experimental process, mainly sample processing, instrumentation, and noise. This variance can cause a bias, affecting the biological conclusions.
Default normalization on Spectronaut™ Pulsar
Spectronaut Pulsar 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 assumes that the samples used are similar, meaning the majority of the precursors within the samples are not regulated and that, for those that are, there is a similar number of peptides up and down-regulated. The algorithm is based on the Local Regression Normalization described by Callister et al. 2006.
Alternative to default normalization
Spectronaut Pulsar default settings are suitable for most, but not all, experimental setups. If you prefer to use alternative settings, Spectronaut Pulsar can also apply global normalization by either the median or the average intensity of all identified peptides. In this case, if you select, for example, global average normalization, the normalized quantity for each peptide is the original quantity minus the run average quantity (average quantity of all peptides of that particular run) plus the global average quantity (average quantity of all peptides in all runs). Alternatively, the normalization can be turned off, for instance, when using dilution series or when analyzing samples with different levels of complexity.
Figure 1. Saving a custom schema in the Settings Perspective
You can change the default normalization strategy in three ways, as explained in the section about how to 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 in the Experiment Setup window
3. For an already analyzed dataset, you can right-click on the experiment tab and choose Settings (figure 3) for the Experiment Setup window to open.
Figure 3. Changing the settings to an already analyzed dataset
To see the effect of the normalization, Spectronaut Pulsar creates a pair of plots where you can visualize your quantitative data throughout your runs before and after the 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 on 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
You can find some more info about the Conditions Editor in the Spectronaut Pulsar manual here.
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.
Created by SEZ. Last update 2018-03-14 by SEZ