apply_thresholds        Apply other thresholds to DE results
detect_outliers_POMA    Outlier detection via POMA R Package
eigenMSNorm             EigenMS Normalization
export_data             Export the SummarizedExperiment object, the
                        meta data, and the normalized data.
extract_consensus_DE_candidates
                        Extract consensus DE candidates
extract_limma_DE        Extract the DE results from eBayes fit of
                        perform_limma function.
filter_out_NA_proteins_by_threshold
                        Filter proteins based on their NA pattern using
                        a specific threshold
filter_out_complete_NA_proteins
                        Remove proteins with NAs in all samples
filter_out_proteins_by_ID
                        Remove proteins by their ID
filter_out_proteins_by_value
                        Remove proteins by value in specific column
get_NA_overview         Function returning some values on the numbers
                        of NA in the data
get_complete_dt         Function to get a long data table of all
                        intensities of all kind of normalization
get_complete_pca_dt     Function to get a long data table of all PCA1
                        and PCA2 values of all kind of normalization
get_normalization_methods
                        Function to return available normalization
                        methods' identifier names
get_overview_DE         Get overview table of DE results
get_proteins_by_value   Get proteins by value in specific column
get_spiked_stats_DE     Get performance metrics of DE results of
                        spike-in data set.
globalIntNorm           Total Intensity Normalization
globalMeanNorm          Total Intensity Normalization Using the Mean
                        for the Calculation of Scaling Factors
globalMedianNorm        Total Intensity Normalization Using the Median
                        for the Calculation of Scaling Factors
impute_se               Method to impute SummarizedExperiment.  This
                        method performs a mixed imputation on the
                        proteins. It uses a k-nearest neighbor
                        imputation for proteins with missing values at
                        random (MAR) and imputes missing values by
                        random draws from a left-shifted Gaussian
                        distribution for proteins with missing values
                        not at random (MNAR).
irsNorm                 Internal Reference Scaling Normalization
limmaNorm               limma::removeBatchEffects (limBE)
load_data               Load real-world proteomics data into a
                        SummarizedExperiment
load_spike_data         Load spike-in proteomics data into a
                        SummarizedExperiment
loessCycNorm            Cyclic Loess Normalization of limma
loessFNorm              Fast Loess Normalization of limma
meanNorm                Mean Normalization
medianAbsDevNorm        Median Absolute Deviation Normalization
medianNorm              Median Normalization
normalize_se            Normalize SummarizedExperiment object using
                        single normalization methods or specified
                        combinations of normalization methods
normalize_se_combination
                        Normalize SummarizedExperiment object using
                        combinations of normalization methods
normalize_se_single     Normalize SummarizedExperiment object using
                        different normalization methods
normicsNorm             Normics Normalization (Normics using VSN or
                        using Median)
perform_DEqMS           Perform DEqMS
perform_ROTS            Performing ROTS
perform_limma           Fitting a linear model using limma
plot_NA_density         Plot the intensity distribution of proteins
                        with and without NAs
plot_NA_frequency       Plot protein identification overlap (x =
                        identified in number of Samples, y=number of
                        proteins)
plot_NA_heatmap         Plot heatmap of the NA pattern
plot_PCA                PCA plot of the normalized data
plot_ROC_AUC_spiked     Plot ROC curve and barplot of AUC values for
                        each method for a specific comparion or for all
                        comparisons
plot_TP_FP_spiked_bar   Barplot of true and false positives for
                        specific comparisons and normalization methods
plot_TP_FP_spiked_box   Boxplot of true and false positives for
                        specific comparisons and normalization methods
plot_TP_FP_spiked_scatter
                        Scatterplot of true positives and false
                        positives (median with errorbars as Q1, and Q3)
                        for all comparisons
plot_boxplots           Plot the distributions of the normalized data
                        as boxplots
plot_condition_overview
                        Barplot showing the number of samples per
                        condition
plot_densities          Plot the densities of the normalized data
plot_fold_changes_spiked
                        Boxplot of log fold changes of spike-in and
                        background proteins for specific normalization
                        methods and comparisons. The ground truth
                        (calculated based on the concentrations of the
                        spike-ins) is shown as a horizontal line.
plot_heatmap            Plot a heatmap of the sample intensities with
                        optional column annotations for a selection of
                        normalization methods
plot_heatmap_DE         Heatmap of DE results
plot_histogram_spiked   Plot histogram of the spike-in and background
                        protein intensities per condition.
plot_identified_spiked_proteins
                        Plot number of identified spike-in proteins per
                        sample.
plot_intersection_enrichment
                        Functional enrichment analysis for analyzing
                        the DE results of different normalization
                        methods and biologically interpreting the
                        results
plot_intragroup_PCV     Plot intragroup pooled coefficient of variation
                        (PCV) of the normalized data
plot_intragroup_PEV     Plot intragroup pooled estimate of variance
                        (PEV) of the normalized data
plot_intragroup_PMAD    Plot intragroup pooled median absolute
                        deviation (PMAD) of the normalized data
plot_intragroup_correlation
                        Plot intragroup correlation of the normalized
                        data
plot_jaccard_heatmap    Jaccard similarity heatmap of DE proteins of
                        the different normalization methods
plot_logFC_thresholds_spiked
                        Line plot of number of true and false positives
                        when applying different logFC thresholds
plot_markers_boxplots   Boxplots of intensities of specific markers
plot_nr_prot_samples    Plot number of non-zero proteins per sample
plot_overview_DE_bar    Overview plots of DE results
plot_overview_DE_tile   Overview heatmap plot of DE results
plot_profiles_spiked    Plot profiles of the spike-in and background
                        proteins using the log2 average protein
                        intensities as a function of the different
                        concentrations.
plot_pvalues_spiked     Boxplot of p-values of spike-in and background
                        proteins for specific normalization methods and
                        comparisons. The ground truth (calculated based
                        on the concentrations of the spike-ins) is
                        shown as a horizontal line.
plot_stats_spiked_heatmap
                        Heatmap of performance metrics for spike-in
                        data sets
plot_tot_int_samples    Plot total protein intensity per sample
plot_upset              Create an UpSet Plot from SummarizedExperiment
                        Data
plot_upset_DE           Upset plots of DE results of the different
                        normalization methods
plot_volcano_DE         Volcano plots of DE results
quantileNorm            Quantile Normalization of preprocessCore
                        package.
readPRONE_example       Helper function to read example data
remove_POMA_outliers    Remove outliers samples detected by the
                        detect_outliers_POMA function
remove_assays_from_SE   Remove normalization assays from a
                        SummarizedExperiment object
remove_reference_samples
                        Remove reference samples of
                        SummarizedExperiment object (reference samples
                        specified during loading)
remove_samples_manually
                        Remove samples with specific value in column
                        manually
rlrMACycNorm            Cyclic Linear Regression Normalization on MA
                        Transformed Data
rlrMANorm               Linear Regression Normalization on MA
                        Transformed Data
rlrNorm                 Robust Linear Regression Normalization of
                        NormalyzerDE.
robnormNorm             RobNorm Normalization
run_DE                  Run DE analysis of a selection of normalized
                        data sets
run_DE_single           Run DE analysis on a single normalized data set
specify_comparisons     Create vector of comparisons for DE analysis
                        (either by single condition (sep = NULL) or by
                        combined condition)
spectraCounteBayes_DEqMS
                        Additional function of the DEqMS package
spike_in_de_res         Example data.table of DE results of a spike-in
                        proteomics data set
spike_in_se             Example SummarizedExperiment of a spike-in
                        proteomics data set
subset_SE_by_norm       Subset SummarizedExperiment object by
                        normalization assays
tmmNorm                 Weighted Trimmed Mean of M Values (TMM)
                        Normalization of edgeR package.
tuberculosis_TMT_de_res
                        Example data.table of DE results of a
                        real-world proteomics data set
tuberculosis_TMT_se     Example SummarizedExperiment of a real-world
                        proteomics data set
vsnNorm                 Variance Stabilization Normalization of limma
                        package.
