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.
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
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"))
If you want to run NormalyzerDE directly from the command line this is possible by executing it through the
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"))'
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.
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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:
normalyzerfunction in the
NormalyzerDE.Rscript is called, starting the process.
inputVerification.R. This results in an instance of the
normMethods.R. This yields an instance of
NormalyzerResultswhich links to the original
NormalyzerDatasetinstance and also contains all the resulting normalized datasets.
normMethods.Rover retention time using functions present in
analyzeResults.R. This yields an instance of
NormalyzerEvaluationResultscontaining the evaluation results. This instance is attached to the
outputUtils.Rwhere the normalizations are written to an output directory, and to
generatePlots.Rwhich contains visualizations for the performance measures. It also uses code in
printPlots.Rto output the results in a desired format.
When a normalized matrix is selected the analysis proceeds to the statistical analysis.
normalyzerdefunction in the
NormalyzerDE.Rscript is called starting the differential expression analysis pipeline.
NormalyzerStatisticsis prepared containing the input data.
calculateStatistics.Rscript is used to calculate the statistical contrasts. The results are attached to the
Any scripts or data that you put into this service are public.
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