About

http://quantitativeproteomics.org/normalyzerde

NormalyzerDE is a software designed to ease the process of selecting an optimal normalization approach for your dataset and to perform subsequent differential expression analysis.

NormalyzerDE includes several normalization approaches, a empirical Bayes-based statistical approach implemented as part of Limma and a newly implemented retention-time segmented normalization approach inspired by previously outlined approaches. The emprical-based based statistics has been shown to increase sensitivity over ANOVA when detecting differentially expressed features.

Cite NormalyzerDE

NormalyzerDE is published here

Willforss, J., Chawade, A., Levander, F. NormalyzerDE: Online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis. Journal of Proteome Research 2018, 10.1021/acs.jproteome.8b00523.

Installation

Currently, the easiest way is to install directly from GitHub. It is recommended that you use R version 3.5 or later as this makes it easier to install the Bioconductor dependencies properly

install.packages("devtools")
devtools::install_github("ComputationalProteomics/NormalyzerDE")

Running NormalyzerDE - Minimal example

library(NormalyzerDE)

Generate normalizations and normalization performance report.

normalyzer(jobName="rscript_norm", designPath="test_design.tsv", dataPath="test_data.tsv")

Calculate differential expression between groups 1-2 and 1-3 (defined in the design matrix).

normalyzerDE(jobName="rscript_de", designPath="test_design.tsv", dataPath="test_data.tsv", comparisons=c("1-2", "1-3"))

For more comprehensive documentation, check the Vignette at NormalyzerDE's Bioconductor page. More information about required input formats is available here.

Executing from command line

If you want to run NormalyzerDE directly from the command line this is possible by executing it through the Rscript command.

Rscript -e 'NormalyzerDE::normalyzer(jobName="rscript_norm", designPath="test_design.tsv", dataPath="test_data.tsv")'
Rscript -e 'NormalyzerDE::normalyzerDE(jobName="rscript_de", designPath="test_design.tsv", dataPath="test_data.tsv", comparisons=c("1-2", "1-3"))'

References

(1) Bolstad, B. preprocessCore: A collection of pre-processing functions. 2018; https://github.com/bmbolstad/preprocessCore.

(2) Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5, R80.

(3) Huber, W.; von Heydebreck, A.; Sultmann, H.; Poustka, A.; Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002, 18, S96–S104.

(4) Kammers, K.; Cole, R. N.; Tiengwe, C.; Ruczinski, I. Detecting significant changes in protein abundance. EuPA Open Proteom. 2015, 7, 11-19.

(5) Lyutvinskiy, Y.; Yang, H.; Rutishauser, D.; Zubarev, R. A. In Silico Instrumental Response Correction Improves Precision of Label-free Proteomics and Accuracy of Proteomics-based Predictive Models. Mol. Cell Proteomics 2013, 12, 2324–2331.

(6) Ritchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47.

(7) van Ooijen, M. P.; Jong, V. L.; Eijkemans, M. J.; Heck, A. J.; Andeweg, A. C.; Binai, N. A.; van den Ham, H.-J. Identification of differentially expressed peptides in high-throughput proteomics data. Brief. Bioinform. 2017, 1–11.

(8) Wolfgang, H. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 2015, 12, 115–121.

Code organization

NormalyzerDE consists of a number of scripts and classes. They are focused around two separate workflows. One is for normalizing and evaluating the normalizations. The second is for performing differential expression analysis. Classes are contained in scripts with the same name.

The standard workflow for the normalization is the following:

• The normalyzer function in the NormalyzerDE.R script is called, starting the process.
• If applicable (that is, input is in Proteois or MaxQuant format), the dataset is preprocessed into the standard format using code in preparsers.R.
• The input is verified to capture standard errors early on using code in inputVerification.R. This results in an instance of the NormalyzerDataset class.
• The data is normalized using several normalization methods present in normMethods.R. This yields an instance of NormalyzerResults which links to the original NormalyzerDataset instance and also contains all the resulting normalized datasets.
• If specified (and if a column with retention time values is present) retention-time segmented approaches are performed by applying normalizations from normMethods.R over retention time using functions present in higherOrderNormMethods.R.
• The results are analyzed using functions present in analyzeResults.R. This yields an instance of NormalyzerEvaluationResults containing the evaluation results. This instance is attached to the NormalyzerResults object.
• The final results are sent to outputUtils.R where the normalizations are written to an output directory, and to generatePlots.R which contains visualizations for the performance measures. It also uses code in printMeta.R and printPlots.R to output the results in a desired format.

When a normalized matrix is selected the analysis proceeds to the statistical analysis.

• The normalyzerde function in the NormalyzerDE.R script is called starting the differential expression analysis pipeline.
• An instance of NormalyzerStatistics is prepared containing the input data.
• Code in the calculateStatistics.R script is used to calculate the statistical contrasts. The results are attached to the NormalyzerStatistics object.
• The resulting statistics are used to generate a report and an annotated output matrix where key statistical measures are attached to the original matrix.