High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT)

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Documentation for package ‘BERT’ version 1.3.6

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

adjustment_step	Adjust a hierarchy level sequentially.
adjust_node	Adjust two batches to each other.
BERT	Adjust data using the BERT algorithm.
chunk_data	Chunks data into n segments with (close-to) equivalent number of batches and stores them in temporary RDS files
compute_asw	Compute the average silhouette width (ASW) for the dataset with respect to both label and batch.
count_existing	Count the number of numeric features in this dataset. Columns labeled "Batch", "Sample" or "Label" will be ignored.
format_DF	Format the data as expected by BERT.
generate_dataset	Generate dataset with batch-effects and biological labels using a simple LS model
generate_data_covariables	Generate dataset with batch-effects and 2 classes with a specified imbalance.
get_adjustable_features	Check, which features contain enough numeric data to be adjusted (at least 2 numeric values)
get_adjustable_features_with_mod	Check, which features contain enough numeric data to be adjusted (at least 2 numeric values per batch and covariate level)
identify_adjustableFeatures_refs	Identifies the adjustable features using only the references. Similar to the function in adjust_features.R but with different arguments
identify_references	Identifies the references to use for this specific batch effect adjustment
ordinal_encode	Ordinal encoding of a vector.
parallel_bert	Adjusts all chunks of data (in parallel) as far as possible.
removeBatchEffectRefs	A method to remove batch effects estimated from a subset (references) per batch only. Source code is heavily based on limma::removeBatchEffects by Gordon Smyth and Carolyn de Graaf
replace_missing	Replaces missing values (NaN) by NA, this appears to be faster
strip_Covariable	Strip column labelled Cov_1 from dataframe.
validate_bert_input	Verifies that the input to BERT is valid.
validate_input_generate_dataset	Validate the user input to the function generate_dataset. Raises an error if and only if the input is malformatted.
verify_references	Verify that the Reference column of the data contains only zeros and ones (if it is present at all)