Package: NormalyzerDE 1.31.0

Jakob Willforss

NormalyzerDE: Evaluation of normalization methods and calculation of differential expression analysis statistics

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.

Authors:Jakob Willforss

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manual.pdf |manual.html✨
card.svg |card.png
NormalyzerDE/json (API)
NEWS

# Install 'NormalyzerDE' in R:
install.packages('NormalyzerDE', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/computationalproteomics/normalyzerde/issues

Pkgdown/docs site:https://computationalproteomics.github.io

On BioConductor:NormalyzerDE-1.31.0(bioc 3.24)NormalyzerDE-1.30.0(bioc 3.23)

normalizationmultiplecomparisonvisualizationbayesianproteomicsmetabolomicsdifferentialexpressionbioconductorbioinformaticslimma

8.89 score 26 stars 1 packages 71 scripts 518 downloads 8 mentions 28 exports 97 dependencies

Last updated from:0c481c1161. Checks:1 NOTE, 9 OK. Indexed: yes.

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bioc-checksNOTE192
linux-devel-x86_64OK346
source / vignettesOK310
linux-release-x86_64OK361
macos-release-arm64OK226
macos-oldrel-arm64OK213
windows-develOK279
windows-releaseOK216
windows-oldrelOK247
wasm-releaseOK138

Exports:analyzeNormalizationscalculateContrastsgenerateAnnotatedMatrixgeneratePlotsgenerateStatsReportgetRTNormalizedMatrixgetSmoothedRTNormalizedMatrixgetVerifiedNormalyzerObjectglobalIntensityNormalizationmeanNormalizationmedianNormalizationnormalyzernormalyzerDENormalyzerEvaluationResultsNormalyzerResultsNormalyzerStatisticsnormMethodsperformCyclicLoessNormalizationperformGlobalRLRNormalizationperformQuantileNormalizationperformSMADNormalizationperformVSNNormalizationreduceTechnicalReplicatessetupJobDirsetupRawContrastObjectsetupRawDataObjectsetupTestDatawriteNormalizedDatasets

Dependencies:abindaffyaffyioapebackportsbase64encBiobaseBiocGenericsBiocManagerbootbroomcarcarDataclicolorspacecowplotcpp11DelayedArrayDerivdigestdoBydplyrfarverforecastFormulafracdiffgenericsGenomicRangesggforceggplot2gluegtableIRangesisobandjsonlitelabelinglatticelifecyclelimmalme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolyclippreprocessCorepurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS4ArraysS4VectorsS7scalesSeqinfoSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsystemfontstibbletidyrtidyselecttimeDatetweenrurcautf8vctrsviridisLitevsnwithrXVectorzoo

Evaluation and statistics of expression data using NormalyzerDE

Jakob Willforss, Aakash Chawade and Fredrik Levander

Rendered fromvignette.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2023-09-11
Started: 2018-09-07

Readme and manuals

Help Manual

Help pageTopics
Calculate measures for normalization resultsanalyzeNormalizations
Performs statistical comparisons between the supplied conditions. It uses the design matrix and data matrix in the supplied NormalyzerStatistics object. A column is supplied specifying which of the columns in the design matrix that is used for deciding the sample groups. The comparisons vector specifies which pairwise comparisons between condition levels that are to be calculated.calculateContrasts calculateContrasts,NormalyzerStatistics-method
Generate an annotated data frame from statistics objectgenerateAnnotatedMatrix
Generates a number of visualizations for the performance measures calculated for the normalized matrices. These contain both general measures and direct comparisons for different normalization approaches.generatePlots
Generate full output report plot document. Plots p-value histograms for each contrast in the NormalyzerStatistics instance and writes these to a PDF report.generateStatsReport
Perform RT-segmented normalization by performing the supplied normalization over retention-time sliced datagetRTNormalizedMatrix
Generate multiple RT time-window normalized matrices where one is shifted. Merge them using a specified method (mean or median) and return the result.getSmoothedRTNormalizedMatrix
Verify that input data is in correct format, and if so, return a generated NormalyzerDE data object from that input datagetVerifiedNormalyzerObject
The normalization divides the intensity of each variable in a sample with the sum of intensities of all variables in the sample and multiplies with the median of sum of intensities of all variables in all samples. The normalized data is then log2-transformed.globalIntensityNormalization
Load raw data into dataframeloadData
Load raw design into dataframeloadDesign
Intensity of each variable in a given sample is divided by the mean of sum of intensities of all variables in the sample and then multiplied by the mean of sum of intensities of all variables in all samples. The normalized data is then transformed to log2.meanNormalization
Intensity of each variable in a given sample is divided by the median of intensities of all variables in the sample and then multiplied by the mean of median of sum of intensities of all variables in all samples. The normalized data is then log2-transformed.medianNormalization
NormalyzerDE pipeline entry pointnormalyzer
NormalyzerDE differential expressionnormalyzerDE
Representation of evaluation results by calculating performance measures for an an NormalyzerResults instanceNormalyzerEvaluationResults
Representation of the results from performing normalization over a datasetNormalyzerResults
Class representing a dataset for statistical processing in NormalyzerDENormalyzerStatistics
Perform normalizations on Normalyzer datasetnormMethods
Cyclic Loess normalizationperformCyclicLoessNormalization
Global linear regression normalizationperformGlobalRLRNormalization
Quantile normalization is performed by the function "normalize.quantiles" from the package preprocessCore.performQuantileNormalization
Median absolute deviation normalization Normalization subtracts the median and divides the data by the median absolute deviation (MAD).performSMADNormalization
Log2 transformed data is normalized using the function "justvsn" from the VSN package.performVSNNormalization
Remove technical replicates from data and designreduceTechnicalReplicates
Create empty directory for runsetupJobDir
Prepare SummarizedExperiment object for statistics datasetupRawContrastObject
Prepare SummarizedExperiment object for raw data to be normalized containing data, design and annotation informationsetupRawDataObject
Write normalization matrices to filewriteNormalizedDatasets