correct_batch_effects {proBatch} | R Documentation

## Batch correction method allows correction of continuous sigal drift within batch and discrete difference across batches.

### Description

Batch correction method allows correction of continuous sigal drift within batch and discrete difference across batches.

### Usage

    correct_batch_effects(data_matrix, sample_annotation,
      fitFunc = "loess_regression", discreteFunc = c("MedianCentering",
      "ComBat"), batch_col = "MS_batch",
      feature_id_col = "peptide_group_label",
      sample_id_col = "FullRunName", measure_col = "Intensity",
      sample_order_col = "order", abs_threshold = 5, pct_threshold = 0.2,
      ...)

### Arguments

`data_matrix` | features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. Usually the log transformed version of the original data
`sample_annotation` | data frame with sample ID, technical (e.g. MS batches) and biological (e.g. Diet) covariates
`fitFunc` | function to use for the fit (currently only `loess_regression` available)
`discreteFunc` | function to use for discrete batch correction (`MedianCentering` or `ComBat`)
`batch_col` | column in `sample_annotation` that should be used for batch comparison
`feature_id_col` | name of the column with feature/gene/peptide/protein ID used in the long format representation df_long. In the wide formatted representation data_matrix this corresponds to the row names.
`sample_id_col` | name of the column in sample_annotation file, where the filenames (colnames of the data matrix are found)
`measure_col` | if `df_long` is among the parameters, it is the column with expression/abundance/intensity, otherwise, it is used internally for consistency
`sample_order_col` | column, determining the order of sample MS run, used as covariate to fit the non-linear fit
`abs_threshold` | the absolute threshold to filter data for curve fitting
`pct_threshold` | the percentage threshold to filter data for curve fitting
`...` | other parameters, usually of `normalize_custom_fit`, and `fit_func`

### Value

`data_matrix`-size data matrix with batch-effect corrected by fit and discrete functions

### Examples

    batch_corrected_matrix <- correct_batch_effects(
    example_proteome_matrix, example_sample_annotation,
    discreteFunc = 'MedianCentering',
    batch_col = 'MS_batch',
    span = 0.7,
    abs_threshold = 5, pct_threshold = 0.20)

[Package proBatch version 1.0.0 Index]