correctBatchEffect {BEclear} | R Documentation

## Correct a batch effect in DNA methylation data

### Description

This method combines most functions of the `BEclear-package` to one. The method performs the whole process of searching for batch effects and automatically correct them for a matrix of beta values stemming from DNA methylation data.

### Usage

    correctBatchEffect(data, samples, adjusted=TRUE, method="fdr",
    mediansTreshold = 0.05, pvaluesTreshold = 0.01, rowBlockSize=60,
    colBlockSize=60, epochs=50, lambda = 1, gamma = 0.01, r = 10,
    outputFormat="", dir=getwd(), BPPARAM=SerialParam(), fixedSeed= TRUE)

### Arguments

`data` | any matrix filled with beta values, column names have to be sample_ids corresponding to the ids listed in "samples", row names have to be gene names.
`samples` | data frame with two columns, the first column has to contain the sample numbers, the second column has to contain the corresponding batch number. Colnames have to be named as "sample_id" and "batch_id".
`adjusted` | should the p-values be adjusted or not, see "method" for available adjustment methods.
`method` | adjustment method for p-value adjustment, default method is "false discovery rate adjustment", for other available methods see the description of the used standard R package `p.adjust`. See `calcBatchEffects` for more information.
`mediansTreshold` | the threshold above or equal median values are regarded as batch effected, when the criteria for p-values is also met.
`pvaluesTreshold` | the threshold below or equal p-values are regarded as batch effected, when the criteria for medians is also met.
`rowBlockSize` | the number of rows that is used in a block if the function is run in parallel mode and/or not on the whole matrix. Set this, and the "colBlockSize" parameter to 0 if you want to run the function on the whole input matrix. See `imputeMissingData` and especially the details section of See `imputeMissingData` for more information about this feature.
`colBlockSize` | the number of columns that is used in a block if the function is run in parallel mode and/or not on the whole matrix. Set this, and the "rowBlockSize" parameter to 0 if you want to run the function on the whole input matrix. See `imputeMissingData` and especially the details section of See `imputeMissingData` for more information about this feature.
`epochs` | the number of iterations used in the gradient descent algorithm to predict the missing entries in the data matrix. See `imputeMissingData` for more information.
`lambda` | constant that controls the extent of regularization during the gradient descent
`gamma` | constant that controls the extent of the shift of parameters during the gradient descent
`r` | length of the second dimension of variable matrices R and L
`outputFormat` | you can choose if the finally returned data matrix should be saved as an .RData file or as a tab-delimited .txt file in the specified directory. Allowed values are "RData" and "txt". See `imputeMissingData` for more information.
`dir` | set the path to a directory the predicted matrix should be stored. The current working directory is defined as default parameter.
`BPPARAM` | An instance of the `BiocParallelParam-class` that determines how to parallelisation of the functions will be evaluated.
`fixedSeed` | determines if they seed should be fixed, which is important for testing

### Details

correctBatchEffect

The function performs the whole process of searching for batch effects and automatically correct them for a matrix of beta values stemming from DNA methylation data. Thereby, the function is running most of the functions from the `BEclear-package` in a logical order.
First, median comparison values are calculated by the `calcBatchEffects` function, followed by the calculation of p-values also by the `calcBatchEffects` function. With the results from the median comparison and p-value calculation, a summary data frame is build using the `calcSummary` function, and a scoring table is established by the `calcScore` function. Now, found entries from the summary are set to NA in the input matrix using the `clearBEgenes` function, then the `imputeMissingData` function is used to predict the missing values and at the end, predicted entries outside the boundaries (values lower than 0 or greater than 1) are corrected using the `replaceOutsideValues` function.

### Value

A list containing the following fields (for detailed information look at the documentations of the corresponding functions):

medians

  A data.frame containing all median comparison values corresponding to the input matrix.

pvalues

  A data.frame containing all p-values corresponding to the input matrix.

summary

  The summarized results of the median comparison and p-value calculation.

score.table

  A data.frame containing the number of found genes and a BEscore for every batch.

cleared.data

  the input matrix with all values defined in the summary set to NA.

predicted.data

  the input matrix after all previously NA values have been predicted.

corrected.predicted.data

  the predicted matrix after the correction for predicted values outside the boundaries.

### References

Akulenko R, Merl M, Helms V (2016). “BEclear: Batch effect detection and adjustment in DNA methylation data.” PLoS ONE, 11(8), 1–17. ISSN 19326203, doi: 10.1371/journal.pone.0159921.

Koren Y, Bell R, Volinsky C (2009). “Matrix Factorization Techniques for Recommender Systems.” Computer, 42(8), 30–37. ISSN 0018-9162, doi: 10.1109/MC.2009.263, doi.ieeecomputersociety.org/10.1109/MC.2009.263 http://ieeexplore.ieee.org/document/5197422/.

CandÃ¨s EJ, Recht B (2009). “Exact Matrix Completion via Convex Optimization.” Foundations of Computational Mathematics, 9(6), 717–772. ISSN 1615-3375, doi: 10.1007/s10208-009-9045-5, http://link.springer.com/10.1007/s10208-009-9045-5.

### See Also

`calcBatchEffects`

`calcSummary`

`calcScore`

`clearBEgenes`

`imputeMissingData`

`replaceOutsideValues`

### Examples

    ## Shortly running example. For a more realistic example that takes
    ## some more time, run the same procedure with the full BEclearData
    ## dataset.

    ## Whole procedure that has to be done to use this function.
    ## Correct the example data for a batch effect
    data(BEclearData)
    ex.data <- ex.data[31:90, 7:26]
    ex.samples <- ex.samples[7:26, ]

    # Note that row- and block sizes are just set to 10 to get a short runtime.
    # To use these parameters, either use the default values or please note the
    # description in the details section of \code{\link{imputeMissingData}}
    result <- correctBatchEffect(
      data = ex.data, samples = ex.samples,
      adjusted = TRUE, method = "fdr", rowBlockSize = 10, colBlockSize = 10,
      epochs = 50, outputFormat = "RData", dir = getwd()
    )

    # Unlist variables
    medians <- result$medians
    pvals <- result$pvals
    summary <- result$summary
    score <- result$score.table
    cleared <- result$clearedData
    predicted <- result$predictedData
    corrected <- result$correctedPredictedData

[Package BEclear version 2.0.0 Index]