Package: NormalyzerDE 1.25.0

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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NEWS

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

Peer review:

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

Datasets:

    On BioConductor:NormalyzerDE-1.25.0(bioc 3.21)NormalyzerDE-1.24.0(bioc 3.20)

    normalizationmultiplecomparisonvisualizationbayesianproteomicsmetabolomicsdifferentialexpressionbioconductorbioinformaticslimma

    7.26 score 20 stars 1 packages 38 scripts 350 downloads 8 mentions 28 exports 97 dependencies

    Last updated 2 months agofrom:cfe738a321. Checks:ERROR: 2 WARNING: 5. Indexed: yes.

    TargetResultDate
    Doc / VignettesFAILNov 30 2024
    R-4.5-winWARNINGNov 30 2024
    R-4.5-linuxERRORNov 30 2024
    R-4.4-winWARNINGNov 30 2024
    R-4.4-macWARNINGNov 30 2024
    R-4.3-winWARNINGNov 30 2024
    R-4.3-macWARNINGNov 30 2024

    Exports:analyzeNormalizationscalculateContrastsgenerateAnnotatedMatrixgeneratePlotsgenerateStatsReportgetRTNormalizedMatrixgetSmoothedRTNormalizedMatrixgetVerifiedNormalyzerObjectglobalIntensityNormalizationmeanNormalizationmedianNormalizationnormalyzernormalyzerDENormalyzerEvaluationResultsNormalyzerResultsNormalyzerStatisticsnormMethodsperformCyclicLoessNormalizationperformGlobalRLRNormalizationperformQuantileNormalizationperformSMADNormalizationperformVSNNormalizationreduceTechnicalReplicatessetupJobDirsetupRawContrastObjectsetupRawDataObjectsetupTestDatawriteNormalizedDatasets

    Dependencies:abindaffyaffyioapeaskpassbackportsBiobaseBiocGenericsBiocManagerbootbroomcarcarDataclicolorspacecowplotcpp11crayoncurlDelayedArrayDerivdigestdoBydplyrfansifarverFormulagenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggforceggplot2gluegtablehttrIRangesisobandjsonlitelabelinglatticelifecyclelimmalme4magrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigpolyclippreprocessCorepurrrquantregR6RColorBrewerRcppRcppEigenrlangS4ArraysS4VectorsscalesSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontstibbletidyrtidyselecttweenrUCSC.utilsutf8vctrsviridisLitevsnwithrXVectorzlibbioc

    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