[,ProBatchFeatures,ANY,ANY,ANY-method
                        Subset 'ProBatchFeatures' objects without
                        dropping metadata.
calculate_feature_CV    Calculate CV distribution for each feature
calculate_peptide_corr_distr
                        Calculate peptide correlation between and
                        within peptides of one protein
calculate_PVCA          Calculate variance distribution by variable
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
convert_annotation_classes
                        Convert factor and numeric columns
correct_batch_effects   Batch correction of normalized data
correct_with_removeBatchEffect_dm
                        Batch effect correction with removeBatchEffect
                        from limma
create_peptide_annotation
                        Prepare peptide annotation from long format
                        data frame
date_to_sample_order    Convert date/time to POSIXct and rank samples
                        by it
dates_to_posix          Convert date/time to POSIXct
define_sample_order     Defining sample order internally
example_ecoli_data      Example multi-center DIA LFQ E. coli proteomics
                        (DIA-NN)
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
                        Plotting peptide measurements
fit_nonlinear           Fit a non-linear trend (currently optimized for
                        LOESS)
get_chain               Retrieve operation chain as vector or single
                        string "combat_on_mediannorm_on_log"
get_operation_log       Access the operation log (structured)
guess_factor_columns_if_needed
                        Guess factors if numeric columns were not
                        provided
handle_factor_numeric_overlap
                        Handle factor columns that are duplicated in
                        numeric_columns
handle_missing_values   Handle missing values in a data matrix
long_to_matrix          Long to wide data format conversion
matrix_to_long          Wide to long conversion
normalize               Data normalization methods
pb_add_level            Add a new level from an external matrix and
                        link to an existing assay
pb_aggregate_level      Aggregate features (e.g., peptide -> protein)
                        and store as new level
pb_as_long              Get current assay as LONG (via
                        proBatch::matrix_to_long)
pb_as_wide              Get an assay matrix (wide)
pb_assay_matrix         Convenience accessor for assay matrix by
                        name/index (returns the 'intensity' assay)
pb_current_assay        Current (latest) assay name
pb_eval                 Evaluate a pipeline and return the matrix,
                        without storing
pb_missing_helpers      Apply 'QFeatures' missing-data helpers to
                        stored assays
pb_pipeline_name        Pretty pipeline name derived from the assay
pb_register_step        Allow to register/override steps at runtime
                        (e.g., map "combat" -> proBatch::combat_dm)
pb_transform            Compute a pipeline and optionally store only
                        the final result
plot_corr_matrix        Visualise correlation matrix
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_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_NA_density         Plot intensity density by missingness
plot_NA_frequency       Plot missing-value frequency distribution
plot_NA_heatmap         Plot missing-value heatmap(s)
plot_PCA                plot PCA plot
plot_peptide_corr_distribution
                        Create violin plot of peptide correlation
                        distribution
plot_protein_corrplot   Peptide correlation matrix (heatmap)
plot_PVCA               Plot variance distribution by variable
plot_PVCA.df            plot PVCA, when the analysis is completed
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
ProBatchFeatures        Construct a ProBatchFeatures object from a wide
                        matrix + sample annotation.
ProBatchFeatures_from_long
                        Construct from LONG df via
                        proBatch::long_to_matrix
ProBatchFeatures-class
                        ProBatchFeatures: QFeatures subclass with
                        operation log, levels/pipelines, and lazy
                        storage
sample_annotation_to_colors
                        Generate colors for sample annotation
transform_raw_data      Functions to log transform raw data before
                        normalization and batch correction
warn_unmapped_columns   Warn about unmapped columns
