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.
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.
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")
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.
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"))'
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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:
normalyzer
function in the NormalyzerDE.R
script is called, starting the process.preparsers.R
.inputVerification.R
. This results in an instance of the NormalyzerDataset
class.normMethods.R
. This yields an instance of NormalyzerResults
which links to the original NormalyzerDataset
instance and also contains all the resulting normalized datasets.normMethods.R
over retention time using functions present in higherOrderNormMethods.R
.analyzeResults.R
. This yields an instance of NormalyzerEvaluationResults
containing the evaluation results. This instance is attached to the NormalyzerResults
object.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.
normalyzerde
function in the NormalyzerDE.R
script is called starting the differential expression analysis pipeline.NormalyzerStatistics
is prepared containing the input data.calculateStatistics.R
script is used to calculate the statistical contrasts. The results are attached to the NormalyzerStatistics
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