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