R: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

# Tools for Diagnostics and Corrections of Batch Effects in Proteomics

## Documentation for package ‘proBatch’ version 1.14.0

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

## Help Pages

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