calculate_PVCA          Calculate variance distribution by variable
calculate_feature_CV    Calculate CV distribution for each feature
calculate_peptide_corr_distr
                        Calculate peptide correlation between and
                        within peptides of one protein
calculate_sample_corr_distr
                        Calculates correlation for all pairs of the
                        samples in data matrix, labels as
                        replicated/same_batch/unrelated in output
                        columns (see "Value").
check_sample_consistency
                        Check if sample annotation is consistent with
                        data matrix and join the two
correct_batch_effects   Batch correction of normalized data
create_peptide_annotation
                        Prepare peptide annotation from long format
                        data frame Create light-weight peptide
                        annotation data frame for selection of
                        illustrative proteins
date_to_sample_order    Convert date/time to POSIXct and rank samples
                        by it
dates_to_posix          Convert data/time to POSIXct
define_sample_order     Defining sample order internally
example_peptide_annotation
                        Peptide annotation data
example_proteome        Example protein data in long format
example_proteome_matrix
                        Example protein data in matrix
example_sample_annotation
                        Sample annotation data version 1
feature_level_diagnostics
                        Ploting peptide measurements
fit_nonlinear           Fit a non-linear trend (currently optimized for
                        LOESS)
long_to_matrix          Long to wide data format conversion
matrix_to_long          Wide to long conversion
normalize               Data normalization methods
plot_CV_distr           Plot CV distribution to compare various steps
                        of the analysis
plot_CV_distr.df        Plot the distribution (boxplots) of per-batch
                        per-step CV of features
plot_PCA                plot PCA plot
plot_PVCA               Plot variance distribution by variable
plot_PVCA.df            plot PVCA, when the analysis is completed
plot_corr_matrix        Visualise correlation matrix
plot_heatmap_diagnostic
                        Plot the heatmap of samples (cols) vs features
                        (rows)
plot_heatmap_generic    Plot the heatmap
plot_hierarchical_clustering
                        cluster the data matrix to visually inspect
                        which confounder dominates
plot_peptide_corr_distribution
                        Create violin plot of peptide correlation
                        distribution
plot_protein_corrplot   Peptide correlation matrix (heatmap)
plot_sample_corr_distribution
                        Create violin plot of sample correlation
                        distribution
plot_sample_corr_heatmap
                        Sample correlation matrix (heatmap)
plot_sample_mean_or_boxplot
                        Plot per-sample mean or boxplots for initial
                        assessment
plot_split_violin_with_boxplot
                        Plot split violin plot (convenient to compare
                        distribution before and after)
prepare_PVCA_df         prepare the weights of Principal Variance
                        Components
proBatch                proBatch: A package for diagnostics and
                        correction of batch effects, primarily in
                        proteomics
sample_annotation_to_colors
                        Generate colors for sample annotation
transform_raw_data      Functions to log transform raw data before
                        normalization and batch correction
