NormalyzerEvaluationResults
                        Representation of evaluation results by
                        calculating performance measures for an an
                        NormalyzerResults instance
NormalyzerResults       Representation of the results from performing
                        normalization over a dataset
NormalyzerStatistics    Class representing a dataset for statistical
                        processing in NormalyzerDE
analyzeNormalizations   Calculate measures for normalization results
calculateContrasts      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.
generateAnnotatedMatrix
                        Generate an annotated data frame from
                        statistics object
generatePlots           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.
generateStatsReport     Generate full output report plot document.
                        Plots p-value histograms for each contrast in
                        the NormalyzerStatistics instance and writes
                        these to a PDF report.
getRTNormalizedMatrix   Perform RT-segmented normalization by
                        performing the supplied normalization over
                        retention-time sliced data
getSmoothedRTNormalizedMatrix
                        Generate multiple RT time-window normalized
                        matrices where one is shifted. Merge them using
                        a specified method (mean or median) and return
                        the result.
getVerifiedNormalyzerObject
                        Verify that input data is in correct format,
                        and if so, return a generated NormalyzerDE data
                        object from that input data
globalIntensityNormalization
                        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.
loadData                Load raw data into dataframe
loadDesign              Load raw design into dataframe
meanNormalization       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.
medianNormalization     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.
normMethods             Perform normalizations on Normalyzer dataset
normalyzer              NormalyzerDE pipeline entry point
normalyzerDE            NormalyzerDE differential expression
performCyclicLoessNormalization
                        Cyclic Loess normalization
performGlobalRLRNormalization
                        Global linear regression normalization
performQuantileNormalization
                        Quantile normalization is performed by the
                        function "normalize.quantiles" from the package
                        preprocessCore.
performSMADNormalization
                        Median absolute deviation normalization
                        Normalization subtracts the median and divides
                        the data by the median absolute deviation
                        (MAD).
performVSNNormalization
                        Log2 transformed data is normalized using the
                        function "justvsn" from the VSN package.
reduceTechnicalReplicates
                        Remove technical replicates from data and
                        design
setupJobDir             Create empty directory for run
setupRawContrastObject
                        Prepare SummarizedExperiment object for
                        statistics data
setupRawDataObject      Prepare SummarizedExperiment object for raw
                        data to be normalized containing data, design
                        and annotation information
writeNormalizedDatasets
                        Write normalization matrices to file
