Evaluation of normalization methods and calculation of differential expression analysis statistics

Documentation for package ‘NormalyzerDE’ version 1.5.1

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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.
calculateContrasts-method	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.
normalyzer	NormalyzerDE pipeline entry point
normalyzerDE	NormalyzerDE differential expression
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
normMethods	Perform normalizations on Normalyzer dataset
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