BERT (Batch-Effect Removal with Trees) offers flexible and efficient batch effect correction of omics data, while providing maximum tolerance to missing values. Tested on multiple datasets from proteomic analyses, BERT offered a typical 5-10x runtime improvement over existing methods, while retaining more numeric values and preserving batch effect reduction quality.
As such, BERT is a valuable preprocessing tool for data analysis workflows, in particular for proteomic data. By providing BERT via Bioconductor, we make this tool available to a wider research community. An accompanying research paper is currently under preparation and will be made public soon.
BERT addresses the same fundamental data integration challenges than the [HarmonizR][https://github.com/HSU-HPC/HarmonizR] package, which is released on Bioconductor in November 2023. However, various algorithmic modications and optimizations of BERT provide better execution time and better data coverage than HarmonizR. Moreover, BERT offers a more user-friendly design and a less error-prone input format.
Please note that our package BERT is neither affiliated with nor related to Bidirectional Encoder Representations from Transformers as published by Google.
Please report any questions and issues in the GitHub forum, the BioConductor forum or directly contact the authors,
Please download and install a current version of R (Windows binaries). You might want to consider installing a development environment as well, e.g. RStudio. Finally, BERT can be installed via Bioconductor using
if (!require("BiocManager", quietly = TRUE)){
install.packages("BiocManager")
}
BiocManager::install("BERT")which will install all required dependencies. To install the development version of BERT, you can use devtools as follows
devtools::install_github("HSU-HPC/BERT")which may require the manual installation of the dependencies sva and limma.
if (!require("BiocManager", quietly = TRUE)){
install.packages("BiocManager")
}
BiocManager::install("sva")
BiocManager::install("limma")As input, BERT requires a dataframe1 Matrices and SummarizedExperiments work as well, but will automatically be converted to dataframes. with samples in rows and features in columns.
For each sample, the respective batch should be indicated by an integer or string in a corresponding column labelled Batch. Missing values should be labelled as NA. A valid example dataframe could look like this:
example = data.frame(feature_1 = stats::rnorm(5), feature_2 = stats::rnorm(5), Batch=c(1,1,2,2,2))
example
#> feature_1 feature_2 Batch
#> 1 -1.1032707 0.4704284 1
#> 2 -0.2483191 1.4546936 1
#> 3 0.5208047 0.9875318 2
#> 4 0.7518292 0.5452024 2
#> 5 -0.5757007 0.3127694 2Note that each batch should contain at least two samples. Optional columns that can be passed are
Label A column with integers or strings indicating the (known) class for each sample.
NA is not allowed. BERT may use this columns and Batch to compute quality metrics after batch effect correction.
Sample A sample name.
This column is ignored by BERT and can be used to provide meta-information for further processing.
Cov_1, Cov_2, …, Cov_x: One or multiple columns with integers, indicating one or several covariate levels. NA is not allowed.
If this(these) column(s) is present, BERT will pass them as covariates to the the underlying batch effect correction method.
As an example, this functionality can be used to preserve differences between healthy/tumorous samples, if some of the batches exhibit strongly variable class distributions.
Note that BERT requires at least two numeric values per batch and unique covariate level to adjust a feature.
Features that don’t satisfy this condition in a specific batch are set to NA for that batch.
Reference A column with integers or strings from \(\mathbb{N}_0\) that indicate, whether a sample should be used for “learning” the transformation for batch effect correction or whether the sample should be co-adjusted using the learned transformation from the other samples.NA is not allowed. This feature can be used, if some batches contain unique classes or samples with unknown classes which would prohibit the usage of covariate columns. If the column contains a 0 for a sample, this sample will be co-adjusted. Otherwise, the sample should contain the respective class (encoded as integer or string). Note that BERT requires at least two references of common class per adjustment step and that the Reference column is mutually exclusive with covariate columns.
Note that BERT tries to find all metadata information for a SummarizedExperiment, including the mandatory batch information, using colData.
For instance, a valid SummarizedExperiment might be defined as
nrows <- 200
ncols <- 8
expr_values <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
# colData also takes all other metadata information, such as Label, Sample,
# Covariables etc.
colData <- data.frame(Batch=c(1,1,1,1,2,2,2,2), Reference=c(1,1,0,0,1,1,0,0))
dataset_raw = SummarizedExperiment::SummarizedExperiment(assays=list(expr=expr_values), colData=colData)BERT can be invoked by importing the BERT library and calling the BERT function.
The batch effect corrected data is returned as a dataframe that mirrors the input dataframe2 In particular, the row and column names are in the same order and the optional columns are preserved..
library(BERT)
# generate test data with 10% missing values as provided by the BERT library
dataset_raw <- generate_dataset(features=60, batches=10, samplesperbatch=10, mvstmt=0.1, classes=2)
# apply BERT
dataset_adjusted <- BERT(dataset_raw)
#> 2025-01-30 17:26:45.623704 INFO::Formatting Data.
#> 2025-01-30 17:26:45.642545 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:26:45.660413 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:26:46.031889 INFO::Found 600 missing values.
#> 2025-01-30 17:26:46.053065 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:26:46.05469 INFO::Done
#> 2025-01-30 17:26:46.05617 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:26:46.083736 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:26:46.086045 INFO::Found 10 batches.
#> 2025-01-30 17:26:46.087626 INFO::Cores argument is not defined or BPPARAM has been specified. Argument corereduction will not be used.
#> 2025-01-30 17:26:47.490464 INFO::Using default BPPARAM
#> 2025-01-30 17:26:47.491397 INFO::Processing subtree level 1
#> 2025-01-30 17:26:58.75851 INFO::Processing subtree level 2
#> 2025-01-30 17:27:08.661372 INFO::Adjusting the last 1 batches sequentially
#> 2025-01-30 17:27:08.663835 INFO::Done
#> 2025-01-30 17:27:08.665136 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:08.672411 INFO::ASW Batch was 0.455043895027107 prior to batch effect correction and is now -0.111151937279984 .
#> 2025-01-30 17:27:08.673672 INFO::ASW Label was 0.363273131884259 prior to batch effect correction and is now 0.829256568366777 .
#> 2025-01-30 17:27:08.675361 INFO::Total function execution time is 23.1446919441223 s and adjustment time is 22.5783998966217 s ( 97.55 )BERT uses the logging library to convey live information to the user during the adjustment procedure.
The algorithm first verifies the shape and suitability of the input dataframe (lines 1-6) before continuing with the actual batch effect correction (lines 8-14).
BERT measure batch effects before and after the correction step by means of the average silhouette score (ASW) with respect to batch and labels (lines 7 and 15).
The ASW Label should increase in a successful batch effect correction, whereas low values (\(\leq 0\)) are desireable for the ASW Batch3 The optimum of ASW Label is 1, which is typically however not achieved on real-world datasets.
Also, the optimum of ASW Batch can vary, depending on the class distributions of the batches..
Finally, BERT prints the total function execution time (including the computation time for the quality metrics).
BERT offers a large number of parameters to customize the batch effect adjustment. The full function call, including all defaults is
BERT(data, cores = NULL, combatmode = 1, corereduction=2, stopParBatches=2, backend="default", method="ComBat", qualitycontrol=TRUE, verify=TRUE, labelname="Label", batchname="Batch", referencename="Reference", samplename="Sample", covariatename=NULL, BPPARAM=NULL, assayname=NULL)In the following, we list the respective meaning of each parameter: - data: The input dataframe/matrix/SummarizedExperiment to adjust.
See Data Preparation for detailed formatting instructions.
- data The data for batch-effect correction.
Must contain at least two samples per batch and 2 features.
cores: BERT uses BiocParallel for parallelization. If the user specifies a value cores, BERT internally creates and uses a new instance of BiocParallelParam, which is however not exhibited to the user. Setting this parameter can speed up the batch effect adjustment considerably, in particular for large datasets and on unix-based operating systems. A value between \(2\) and \(4\) is a reasonable choice for typical commodity hardware. Multi-node computations are not supported as of now. If, however, cores is not specified, BERT will default to BiocParallel::bpparam(), which may have been set by the user or the system. Additionally, the user can directly specify a specific instance of BiocParallelParam to be used via the BPPARAM argument.combatmode An integer that encodes the parameters to use for ComBat.| Value | par.prior | mean.only |
|---|---|---|
| 1 | TRUE | FALSE |
| 2 | TRUE | TRUE |
| 3 | FALSE | FALSE |
| 4 | FALSE | TRUE |
The value of this parameter will be ignored, if method!="ComBat".
corereduction Positive integer indicating the factor by which the number of processes should be reduced, once no further adjustment is possible for the current number of batches.4 E.g. consider a BERT call with 8 batches and 8 processes.
Further adjustment is not possible with this number of processes, since batches are always processed in pairs.
With corereduction=2, the number of processes for the following adjustment steps would be set to \(8/2=4\), which is the maximum number of usable processes for this example.
This parameter is used only, if the user specified a custom value for parameter cores.
stopParBatches Positive integer indicating the minimum number of batches required at a hierarchy level to proceed with parallelized adjustment.
If the number of batches is smaller, adjustment will be performed sequentially to avoid communication overheads.
backend: The backend to use for inter-process communication.
Possible choices are default and file, where the former refers to the default communication backend of the requested parallelization mode and the latter will create temporary .rds files for data communication.
‘default’ is usually faster for small to medium sized datasets.
method: The method to use for the underlying batch effect correction steps.
Should be either ComBat, limma for limma::removeBatchEffects or ref for adjustment using specified references (cf. Data Preparation).
The underlying batch effect adjustment method for ref is a modified version of the limma method.
qualitycontrol: A boolean to (de)activate the ASW computation.
Deactivating the ASW computations accelerates the computations.
verify: A boolean to (de)activate the initial format check of the input data.
Deactivating this verification step accelerates the computations.
labelname: A string containing the name of the column to use as class labels.
The default is “Label”.
batchname: A string containing the name of the column to use as batch labels.
The default is “Batch”.
referencename: A string containing the name of the column to use as reference labels.
The default is “Reference”.
covariatename: A vector containing the names of columns with categorical covariables.The default is NULL, in which case all column names are matched agains the pattern “Cov”.
BPPARAM: An instance of BiocParallelParam that will be used for parallelization. The default is null, in which case the value of cores determines the behaviour of BERT.
assayname: If the user chooses to pass a SummarizedExperiment object, they need to specify the name of the assay that they want to apply BERT to here.
BERT then returns the input SummarizedExperiment with an additional assay labeled assayname_BERTcorrected.
BERT utilizes the logging package for output.
The user can easily specify the verbosity of BERT by setting the global logging level in the script.
For instance
logging::setLevel("WARN") # set level to warn and upwards
result <- BERT(data,cores = 1) # BERT executes silentlyBERT exhibits a large number of parameters for parallelisation as to provide users with maximum flexibility. For typical scenarios, however, the default parameters are well suited. For very large experiments (\(>15\) batches), we recommend to increase the number of cores (a reasonable value is \(4\) but larger values may be possible on your hardware). Most users should leave all parameters to their respective default.
In the following, we present simple cookbook examples for BERT usage. Note that ASWs (and runtime) will most likely differ on your machine, since the data generating process involves multiple random choices.
Here, BERT uses limma as underlying batch effect correction algorithm (method='limma') and performs all computations on a single process (cores parameter is left on default).
# import BERT
library(BERT)
# generate data with 30 batches, 60 features, 15 samples per batch, 15% missing values and 2 classes
dataset_raw <- generate_dataset(features=60, batches=20, samplesperbatch=15, mvstmt=0.15, classes=2)
# BERT
dataset_adjusted <- BERT(dataset_raw, method="limma")
#> 2025-01-30 17:27:08.800446 INFO::Formatting Data.
#> 2025-01-30 17:27:08.801632 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:27:08.802992 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:27:08.805264 INFO::Found 2700 missing values.
#> 2025-01-30 17:27:08.825552 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:27:08.826527 INFO::Done
#> 2025-01-30 17:27:08.827315 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:27:08.844299 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:27:08.845446 INFO::Found 20 batches.
#> 2025-01-30 17:27:08.846246 INFO::Cores argument is not defined or BPPARAM has been specified. Argument corereduction will not be used.
#> 2025-01-30 17:27:08.850511 INFO::Using default BPPARAM
#> 2025-01-30 17:27:08.851317 INFO::Processing subtree level 1
#> 2025-01-30 17:27:11.690108 INFO::Processing subtree level 2
#> 2025-01-30 17:27:14.54085 INFO::Processing subtree level 3
#> 2025-01-30 17:27:17.434031 INFO::Adjusting the last 1 batches sequentially
#> 2025-01-30 17:27:17.436455 INFO::Done
#> 2025-01-30 17:27:17.437753 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:17.460487 INFO::ASW Batch was 0.476133450227674 prior to batch effect correction and is now -0.130200669914381 .
#> 2025-01-30 17:27:17.461878 INFO::ASW Label was 0.289021177777027 prior to batch effect correction and is now 0.836145842736224 .
#> 2025-01-30 17:27:17.463393 INFO::Total function execution time is 8.66295409202576 s and adjustment time is 8.59115791320801 s ( 99.17 )Here, BERT uses ComBat as underlying batch effect correction algorithm (method is left on default) and performs all computations on a 2 processes (cores=2).
# import BERT
library(BERT)
# generate data with 30 batches, 60 features, 15 samples per batch, 15% missing values and 2 classes
dataset_raw <- generate_dataset(features=60, batches=20, samplesperbatch=15, mvstmt=0.15, classes=2)
# BERT
dataset_adjusted <- BERT(dataset_raw, cores=2)
#> 2025-01-30 17:27:17.531466 INFO::Formatting Data.
#> 2025-01-30 17:27:17.532569 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:27:17.533865 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:27:17.535855 INFO::Found 2700 missing values.
#> 2025-01-30 17:27:17.555587 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:27:17.556582 INFO::Done
#> 2025-01-30 17:27:17.557377 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:27:17.574317 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:27:17.575486 INFO::Found 20 batches.
#> 2025-01-30 17:27:18.154042 INFO::Set up parallel execution backend with 2 workers
#> 2025-01-30 17:27:18.155694 INFO::Processing subtree level 1 with 20 batches using 2 cores.
#> 2025-01-30 17:27:27.5541 INFO::Adjusting the last 2 batches sequentially
#> 2025-01-30 17:27:27.55552 INFO::Adjusting sequential tree level 1 with 2 batches
#> 2025-01-30 17:27:29.619918 INFO::Done
#> 2025-01-30 17:27:29.621443 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:29.645618 INFO::ASW Batch was 0.478351206823234 prior to batch effect correction and is now -0.135751466116489 .
#> 2025-01-30 17:27:29.647208 INFO::ASW Label was 0.300063408895601 prior to batch effect correction and is now 0.84308332248295 .
#> 2025-01-30 17:27:29.64896 INFO::Total function execution time is 12.1174759864807 s and adjustment time is 12.0439939498901 s ( 99.39 )Here, BERT takes the input data using a SummarizedExperiment instead.
Batch effect correction is then performed using ComBat as underlying algorithm (method is left on default) and all computations are performed on a single process (cores parameter is left on default).
nrows <- 200
ncols <- 8
# SummarizedExperiments store samples in columns and features in rows (in contrast to BERT).
# BERT will automatically account for this.
expr_values <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
# colData also takes further metadata information, such as Label, Sample,
# Reference or Covariables
colData <- data.frame("Batch"=c(1,1,1,1,2,2,2,2), "Label"=c(1,2,1,2,1,2,1,2), "Sample"=c(1,2,3,4,5,6,7,8))
dataset_raw = SummarizedExperiment::SummarizedExperiment(assays=list(expr=expr_values), colData=colData)
dataset_adjusted = BERT(dataset_raw, assayname = "expr")
#> 2025-01-30 17:27:29.749442 INFO::Formatting Data.
#> 2025-01-30 17:27:29.750789 INFO::Recognized SummarizedExperiment
#> 2025-01-30 17:27:29.75192 INFO::Typecasting input to dataframe.
#> 2025-01-30 17:27:29.786765 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:27:29.78828 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:27:29.791409 INFO::Found 0 missing values.
#> 2025-01-30 17:27:29.79722 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:27:29.798204 INFO::Done
#> 2025-01-30 17:27:29.799023 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:27:29.803775 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:27:29.804868 INFO::Found 2 batches.
#> 2025-01-30 17:27:29.814373 INFO::Cores argument is not defined or BPPARAM has been specified. Argument corereduction will not be used.
#> 2025-01-30 17:27:29.815514 INFO::Using default BPPARAM
#> 2025-01-30 17:27:29.816399 INFO::Adjusting the last 2 batches sequentially
#> 2025-01-30 17:27:29.817837 INFO::Adjusting sequential tree level 1 with 2 batches
#> 2025-01-30 17:27:29.870816 INFO::Done
#> 2025-01-30 17:27:29.871871 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:29.877017 INFO::ASW Batch was -0.00489372291543187 prior to batch effect correction and is now -0.091514039146912 .
#> 2025-01-30 17:27:29.877951 INFO::ASW Label was 0.00382735722097608 prior to batch effect correction and is now 0.0219566604924808 .
#> 2025-01-30 17:27:29.879068 INFO::Total function execution time is 0.129605054855347 s and adjustment time is 0.0660898685455322 s ( 50.99 )BERT can utilize categorical covariables that are specified in columns Cov_1, Cov_2, ....
These columns are automatically detected and integrated into the batch effect correction process.
# import BERT
library(BERT)
# set seed for reproducibility
set.seed(1)
# generate data with 5 batches, 60 features, 30 samples per batch, 15% missing values and 2 classes
dataset_raw <- generate_dataset(features=60, batches=5, samplesperbatch=30, mvstmt=0.15, classes=2)
# create covariable column with 2 possible values, e.g. male/female condition
dataset_raw["Cov_1"] = sample(c(1,2), size=dim(dataset_raw)[1], replace=TRUE)
# BERT
dataset_adjusted <- BERT(dataset_raw)
#> 2025-01-30 17:27:29.954273 INFO::Formatting Data.
#> 2025-01-30 17:27:29.956079 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:27:29.958298 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:27:29.962102 INFO::Found 1350 missing values.
#> 2025-01-30 17:27:29.964181 INFO::BERT requires at least 2 numeric values per batch/covariate level. This may reduce the number of adjustable features considerably, depending on the quantification technique.
#> 2025-01-30 17:27:30.034926 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:27:30.036296 INFO::Done
#> 2025-01-30 17:27:30.037603 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:27:30.047378 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:27:30.052101 INFO::Found 5 batches.
#> 2025-01-30 17:27:30.052983 INFO::Cores argument is not defined or BPPARAM has been specified. Argument corereduction will not be used.
#> 2025-01-30 17:27:30.053881 INFO::Using default BPPARAM
#> 2025-01-30 17:27:30.054692 INFO::Processing subtree level 1
#> 2025-01-30 17:27:39.710397 INFO::Adjusting the last 2 batches sequentially
#> 2025-01-30 17:27:39.712098 INFO::Adjusting sequential tree level 1 with 2 batches
#> 2025-01-30 17:27:39.753019 INFO::Done
#> 2025-01-30 17:27:39.753982 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:39.760597 INFO::ASW Batch was 0.492773245691086 prior to batch effect correction and is now -0.0377157224767566 .
#> 2025-01-30 17:27:39.76145 INFO::ASW Label was 0.40854766060101 prior to batch effect correction and is now 0.895560693013661 .
#> 2025-01-30 17:27:39.7624 INFO::Total function execution time is 9.80834197998047 s and adjustment time is 9.70111489295959 s ( 98.91 )In rare cases, class distributions across experiments may be severely skewed.
In particular, a batch might contain classes that other batches don’t contain.
In these cases, samples of common conditions may serve as references (bridges) between the batches (method="ref").
BERT utilizes those samples as references that have a condition specified in the “Reference” column of the input.
All other samples are co-adjusted.
Please note, that this strategy implicitly uses limma as underlying batch effect correction algorithm.
# import BERT
library(BERT)
# generate data with 4 batches, 6 features, 15 samples per batch, 15% missing values and 2 classes
dataset_raw <- generate_dataset(features=6, batches=4, samplesperbatch=15, mvstmt=0.15, classes=2)
# create reference column with default value 0. The 0 indicates, that the respective sample should be co-adjusted only.
dataset_raw[, "Reference"] <- 0
# randomly select 2 references per batch and class - in practice, this choice will be determined by external requirements (e.g. class known for only these samples)
batches <- unique(dataset_raw$Batch) # all the batches
for(b in batches){ # iterate over all batches
# references from class 1
ref_idx = sample(which((dataset_raw$Batch==b)&(dataset_raw$Label==1)), size=2, replace=FALSE)
dataset_raw[ref_idx, "Reference"] <- 1
# references from class 2
ref_idx = sample(which((dataset_raw$Batch==b)&(dataset_raw$Label==2)), size=2, replace=FALSE)
dataset_raw[ref_idx, "Reference"] <- 2
}
# BERT
dataset_adjusted <- BERT(dataset_raw, method="ref")
#> 2025-01-30 17:27:39.837507 INFO::Formatting Data.
#> 2025-01-30 17:27:39.838618 INFO::Replacing NaNs with NAs.
#> 2025-01-30 17:27:39.839786 INFO::Removing potential empty rows and columns
#> 2025-01-30 17:27:39.840985 INFO::Found 60 missing values.
#> 2025-01-30 17:27:39.844178 INFO::Introduced 0 missing values due to singular proteins at batch/covariate level.
#> 2025-01-30 17:27:39.844961 INFO::Done
#> 2025-01-30 17:27:39.845711 INFO::Acquiring quality metrics before batch effect correction.
#> 2025-01-30 17:27:39.848876 INFO::Starting hierarchical adjustment
#> 2025-01-30 17:27:39.849821 INFO::Found 4 batches.
#> 2025-01-30 17:27:39.850591 INFO::Cores argument is not defined or BPPARAM has been specified. Argument corereduction will not be used.
#> 2025-01-30 17:27:39.853202 INFO::Using default BPPARAM
#> 2025-01-30 17:27:39.853959 INFO::Processing subtree level 1
#> 2025-01-30 17:27:42.854477 INFO::Adjusting the last 2 batches sequentially
#> 2025-01-30 17:27:42.856584 INFO::Adjusting sequential tree level 1 with 2 batches
#> 2025-01-30 17:27:42.883117 INFO::Done
#> 2025-01-30 17:27:42.884622 INFO::Acquiring quality metrics after batch effect correction.
#> 2025-01-30 17:27:42.890103 INFO::ASW Batch was 0.440355021914032 prior to batch effect correction and is now -0.087480278736629 .
#> 2025-01-30 17:27:42.891525 INFO::ASW Label was 0.373906827748893 prior to batch effect correction and is now 0.919791677398366 .
#> 2025-01-30 17:27:42.893083 INFO::Total function execution time is 3.05557894706726 s and adjustment time is 3.03335094451904 s ( 99.27 )Issues can be reported in the GitHub forum, the BioConductor forum or directly to the authors.
This code is published under the GPLv3.0 License and is available for non-commercial academic purposes.
Please cite our manuscript, if you use BERT for your research: Schumann Y, Gocke A, Neumann J (2024). Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets. PROTEOMICS. ISSN 1615-9861, doi:10.1002/pmic.202400100
sessionInfo()
#> R Under development (unstable) (2025-01-21 r87610 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BERT_1.3.6 BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 blob_1.2.4
#> [3] Biostrings_2.75.3 fastmap_1.2.0
#> [5] janitor_2.2.1 XML_3.99-0.18
#> [7] digest_0.6.37 timechange_0.3.0
#> [9] lifecycle_1.0.4 cluster_2.1.8
#> [11] statmod_1.5.0 survival_3.8-3
#> [13] KEGGREST_1.47.0 invgamma_1.1
#> [15] RSQLite_2.3.9 magrittr_2.0.3
#> [17] genefilter_1.89.0 compiler_4.5.0
#> [19] rlang_1.1.5 sass_0.4.9
#> [21] tools_4.5.0 yaml_2.3.10
#> [23] knitr_1.49 S4Arrays_1.7.1
#> [25] bit_4.5.0.1 DelayedArray_0.33.4
#> [27] abind_1.4-8 BiocParallel_1.41.0
#> [29] BiocGenerics_0.53.6 grid_4.5.0
#> [31] stats4_4.5.0 xtable_1.8-4
#> [33] edgeR_4.5.2 iterators_1.0.14
#> [35] logging_0.10-108 SummarizedExperiment_1.37.0
#> [37] cli_3.6.3 rmarkdown_2.29
#> [39] crayon_1.5.3 generics_0.1.3
#> [41] httr_1.4.7 DBI_1.2.3
#> [43] cachem_1.1.0 stringr_1.5.1
#> [45] splines_4.5.0 parallel_4.5.0
#> [47] AnnotationDbi_1.69.0 BiocManager_1.30.25
#> [49] XVector_0.47.2 matrixStats_1.5.0
#> [51] vctrs_0.6.5 Matrix_1.7-2
#> [53] jsonlite_1.8.9 sva_3.55.0
#> [55] bookdown_0.42 comprehenr_0.6.10
#> [57] IRanges_2.41.2 S4Vectors_0.45.2
#> [59] bit64_4.6.0-1 locfit_1.5-9.10
#> [61] foreach_1.5.2 limma_3.63.3
#> [63] jquerylib_0.1.4 snow_0.4-4
#> [65] annotate_1.85.0 glue_1.8.0
#> [67] codetools_0.2-20 lubridate_1.9.4
#> [69] stringi_1.8.4 GenomeInfoDb_1.43.4
#> [71] GenomicRanges_1.59.1 UCSC.utils_1.3.1
#> [73] htmltools_0.5.8.1 GenomeInfoDbData_1.2.13
#> [75] R6_2.5.1 evaluate_1.0.3
#> [77] lattice_0.22-6 Biobase_2.67.0
#> [79] png_0.1-8 memoise_2.0.1
#> [81] snakecase_0.11.1 bslib_0.8.0
#> [83] SparseArray_1.7.4 nlme_3.1-167
#> [85] mgcv_1.9-1 xfun_0.50
#> [87] MatrixGenerics_1.19.1