# Tools for Diagnostics and Corrections of Batch Effects in Proteomics

## Documentation for package ‘proBatch’ version 2.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A C D E F G H L M N P Q S T U W misc

proBatch-package | proBatch: A package for diagnostics and correction of batch effects, primarily in proteomics

## -- A --

adjust_batch_trend_df | Batch correction of normalized data
adjust_batch_trend_dm | Batch correction of normalized data

## -- C --

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_PVCA.default | Calculate variance distribution by variable
calculate_PVCA.ProBatchFeatures | 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").
center_feature_batch_means_df | Batch correction of normalized data
center_feature_batch_means_dm | Batch correction of normalized data
center_feature_batch_medians_df | Batch correction of normalized data
center_feature_batch_medians_dm | Batch correction of normalized data
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_batch_effects_df | Batch correction of normalized data
correct_batch_effects_dm | Batch correction of normalized data
correct_with_ComBat_df | Batch correction of normalized data
correct_with_ComBat_dm | 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

## -- D --

dates_to_posix | Convert date/time to POSIXct
date_to_sample_order | Convert date/time to POSIXct and rank samples by it
define_sample_order | Defining sample order internally

## -- E --

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

## -- F --

feature_level_diagnostics | Plotting peptide measurements
fit_nonlinear | Fit a non-linear trend (currently optimized for LOESS)

## -- G --

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

## -- H --

handle_factor_numeric_overlap | Handle factor columns that are duplicated in numeric_columns
handle_missing_values | Handle missing values in a data matrix

## -- L --

log_transform_df | Functions to log transform raw data before normalization and batch correction
log_transform_dm | Functions to log transform raw data before normalization and batch correction
log_transform_dm.default | Functions to log transform raw data before normalization and batch correction
log_transform_dm.ProBatchFeatures | Functions to log transform raw data before normalization and batch correction
long_to_matrix | Long to wide data format conversion

## -- M --

matrix_to_long | Wide to long conversion

## -- N --

normalize | Data normalization methods
normalize_data_df | Data normalization methods
normalize_data_dm | Data normalization methods
normalize_sample_medians_df | Data normalization methods
normalize_sample_medians_dm | Data normalization methods

## -- P --

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_assay_matrix | Convenience accessor for assay matrix by name/index (returns the 'intensity' assay)
pb_as_long | Get current assay as LONG (via proBatch::matrix_to_long)
pb_as_wide | Get an assay matrix (wide)
pb_current_assay | Current (latest) assay name
pb_eval | Evaluate a pipeline and return the matrix, without storing
pb_filterNA | Apply 'QFeatures' missing-data helpers to stored assays
pb_infIsNA | Apply 'QFeatures' missing-data helpers to stored assays
pb_missing_helpers | Apply 'QFeatures' missing-data helpers to stored assays
pb_nNA | 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
pb_zeroIsNA | Apply 'QFeatures' missing-data helpers to stored assays
plot_boxplot | Plot per-sample mean or boxplots for initial assessment
plot_boxplot.default | Plot per-sample mean or boxplots for initial assessment
plot_boxplot.ProBatchFeatures | Plot per-sample mean or boxplots for initial assessment
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_diagnostic.default | Plot the heatmap of samples (cols) vs features (rows)
plot_heatmap_diagnostic.ProBatchFeatures | Plot the heatmap of samples (cols) vs features (rows)
plot_heatmap_generic | Plot the heatmap
plot_heatmap_generic.default | Plot the heatmap
plot_heatmap_generic.ProBatchFeatures | Plot the heatmap
plot_hierarchical_clustering | cluster the data matrix to visually inspect which confounder dominates
plot_hierarchical_clustering.default | cluster the data matrix to visually inspect which confounder dominates
plot_hierarchical_clustering.ProBatchFeatures | cluster the data matrix to visually inspect which confounder dominates
plot_iRT | Plotting peptide measurements
plot_NA_density | Plot intensity density by missingness
plot_NA_density.default | Plot intensity density by missingness
plot_NA_density.ProBatchFeatures | Plot intensity density by missingness
plot_NA_frequency | Plot missing-value frequency distribution
plot_NA_frequency.default | Plot missing-value frequency distribution
plot_NA_frequency.ProBatchFeatures | Plot missing-value frequency distribution
plot_NA_heatmap | Plot missing-value heatmap(s)
plot_NA_heatmap.default | Plot missing-value heatmap(s)
plot_NA_heatmap.ProBatchFeatures | Plot missing-value heatmap(s)
plot_PCA | plot PCA plot
plot_PCA.default | plot PCA plot
plot_PCA.ProBatchFeatures | plot PCA plot
plot_peptides_of_one_protein | Plotting peptide measurements
plot_peptide_corr_distribution | Create violin plot of peptide correlation distribution
plot_peptide_corr_distribution.corrDF | Create violin plot of peptide correlation distribution
plot_protein_corrplot | Peptide correlation matrix (heatmap)
plot_PVCA | Plot variance distribution by variable
plot_PVCA.default | Plot variance distribution by variable
plot_PVCA.df | plot PVCA, when the analysis is completed
plot_PVCA.ProBatchFeatures | Plot variance distribution by variable
plot_sample_corr_distribution | Create violin plot of sample correlation distribution
plot_sample_corr_distribution.corrDF | Create violin plot of sample correlation distribution
plot_sample_corr_heatmap | Sample correlation matrix (heatmap)
plot_sample_mean | Plot per-sample mean or boxplots for initial assessment
plot_sample_mean.default | Plot per-sample mean or boxplots for initial assessment
plot_sample_mean.ProBatchFeatures | Plot per-sample mean or boxplots for initial assessment
plot_sample_mean_or_boxplot | Plot per-sample mean or boxplots for initial assessment
plot_single_feature | Plotting peptide measurements
plot_spike_in | Plotting peptide measurements
plot_split_violin_with_boxplot | Plot split violin plot (convenient to compare distribution before and after)
plot_with_fitting_curve | Plotting peptide measurements
prepare_PVCA_df | prepare the weights of Principal Variance Components
prepare_PVCA_df.default | prepare the weights of Principal Variance Components
prepare_PVCA_df.ProBatchFeatures | 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-class | ProBatchFeatures: QFeatures subclass with operation log, levels/pipelines, and lazy storage
ProBatchFeatures-subset | Subset 'ProBatchFeatures' objects without dropping metadata.
ProBatchFeatures_from_long | Construct from LONG df via proBatch::long_to_matrix

## -- Q --

quantile_normalize_df | Data normalization methods
quantile_normalize_dm | Data normalization methods

## -- S --

sample_annotation_to_colors | Generate colors for sample annotation
sample_annotation_to_colors.default | Generate colors for sample annotation
sample_annotation_to_colors.ProBatchFeatures | Generate colors for sample annotation

## -- T --

transform_raw_data | Functions to log transform raw data before normalization and batch correction

## -- U --

unlog_df | Functions to log transform raw data before normalization and batch correction
unlog_dm | Functions to log transform raw data before normalization and batch correction
unlog_dm.default | Functions to log transform raw data before normalization and batch correction
unlog_dm.ProBatchFeatures | Functions to log transform raw data before normalization and batch correction

## -- W --

warn_unmapped_columns | Warn about unmapped columns

## -- misc --

[-method | Subset 'ProBatchFeatures' objects without dropping metadata.