Tools for Diagnostics and Corrections of Batch Effects in Proteomics

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Documentation for package ‘proBatch’ version 1.4.0

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adjust_batch_trend_df Batch correction of normalized data
adjust_batch_trend_dm Batch correction of normalized data
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").
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
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
create_peptide_annotation Prepare peptide annotation from long format data frame Create light-weight peptide annotation data frame for selection of illustrative proteins
dates_to_posix Convert data/time to POSIXct
date_to_sample_order Convert date/time to POSIXct and rank samples by it
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)
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
long_to_matrix Long to wide data format conversion
matrix_to_long Wide to long conversion
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
plot_boxplot 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_generic Plot the heatmap
plot_hierarchical_clustering cluster the data matrix to visually inspect which confounder dominates
plot_iRT Ploting peptide measurements
plot_PCA plot PCA plot
plot_peptides_of_one_protein Ploting 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.df plot PVCA, when the analysis is completed
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_or_boxplot Plot per-sample mean or boxplots for initial assessment
plot_single_feature Ploting peptide measurements
plot_spike_in Ploting peptide measurements
plot_split_violin_with_boxplot Plot split violin plot (convenient to compare distribution before and after)
plot_with_fitting_curve Ploting peptide measurements
prepare_PVCA_df prepare the weights of Principal Variance Components
proBatch proBatch: A package for diagnostics and correction of batch effects, primarily in proteomics
quantile_normalize_df Data normalization methods
quantile_normalize_dm Data normalization methods
sample_annotation_to_colors Generate colors for sample annotation
transform_raw_data Functions to log transform raw data before normalization and batch correction
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