Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Documentation for package ‘proBatch’ version 1.99.4

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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.df.default	plot PVCA, when the analysis is completed
plot_PVCA.df.ProBatchFeatures	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.