Package index • NormalyzerDE

Main workflows

normalyzerDE(): NormalyzerDE differential expression
normalyzer(): NormalyzerDE pipeline entry point
analyzeNormalizations(): Calculate measures for normalization results
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.
writeNormalizedDatasets(): Write normalization matrices to file

Results objects

NormalyzerResults(): Representation of the results from performing normalization over a dataset
NormalyzerEvaluationResults(): Representation of evaluation results by calculating performance measures for an an NormalyzerResults instance
NormalyzerStatistics(): Class representing a dataset for statistical processing in NormalyzerDE

Input and setup

loadData(): Load raw data into dataframe
loadDesign(): Load raw design into dataframe
setupJobDir(): Create empty directory for run
setupRawDataObject(): Prepare SummarizedExperiment object for raw data to be normalized containing data, design and annotation information
setupRawContrastObject(): Prepare SummarizedExperiment object for statistics data
reduceTechnicalReplicates(): Remove technical replicates from data and design
getVerifiedNormalyzerObject(): Verify that input data is in correct format, and if so, return a generated NormalyzerDE data object from that input data
generateAnnotatedMatrix(): Generate an annotated data frame from statistics object
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.

Normalization methods

normMethods(): Perform normalizations on Normalyzer dataset
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.
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.
performQuantileNormalization(): Quantile normalization is performed by the function "normalize.quantiles" from the package preprocessCore.
performVSNNormalization(): Log2 transformed data is normalized using the function "justvsn" from the VSN package.
performCyclicLoessNormalization(): Cyclic Loess normalization
performSMADNormalization(): Median absolute deviation normalization Normalization subtracts the median and divides the data by the median absolute deviation (MAD).
performGlobalRLRNormalization(): Global linear regression normalization

Differential expression

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.