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. 2014 Jun 6;13(6):3114-20.
doi: 10.1021/pr401264n. Epub 2014 May 2.

Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets

Aakash Chawade  1 , Erik Alexandersson, Fredrik Levander
Affiliations

"V体育官网" Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets

"V体育官网" Aakash Chawade et al. J Proteome Res. .

Abstract

High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics. org/normalyzer . VSports手机版.

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Figures

Figure 1
Figure 1
Normalyzer workflow highlighting types of input data, normalization, analysis methods, and final output.
Figure 2
Figure 2
Case study 1. Benchmark data generated by shotgun proteomics. (a) Summed raw intensity from all peptides in each sample. (b) Relative pooled intragroup coefficient of variation (PCV). For percentage estimation, PCV in the un-normalized log2 transformed data is considered as 100%. (c) Mean R2 values generated from observed and theoretical values for the UPS1 peptides in the dilution series. (d) Receiver operating characteristics (ROC) curves generated from the UPS1 proteins from differently normalized data sets with one-way ANOVA. UPS1 proteins were considered true positives, and the background proteins were considered true negatives.
Figure 3
Figure 3
Case study 2. Benchmark data generated by Affymetrix microarray. (a) Percent PCV averaged over all groups. For percentage estimation, variability in un-normalized log2 transformed data is considered as 100%. (b) MeanSDplot of VSN-G and VSN-R normalized data. (c) ROC curves generated from the spiked-in probe sets from differently normalized data sets with one-way ANOVA.
Figure 4
Figure 4
Case study 3. Biological data generated by shotgun proteomics from P. infestans infected potato leaves. (a) Summed raw intensity from all peptides in each samples. (b) Summed missing values in samples. (c) Relative PCV. (d) RLE plots for selected data sets. (e) One-way ANOVA (FDR < 0.05) and (f) Kruskal–Wallis test for statistical significance (FDR < 0.05).
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References

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