process {DEP} | R Documentation

## Proteomics data processing

### Description

`process` performs data processing on a SummarizedExperiment object. It (1) filters a proteomics dataset based on missing values, (2) applies variance stabilizing normalization and (3) imputes eventual remaining missing values.

### Usage

    process(se, thr = 0, fun = c("man", "bpca", "knn", "QRILC", "MLE",
      "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), ...)

### Arguments

`se` | SummarizedExperiment, Proteomics data with unique names and identifiers annotated in 'name' and 'ID' columns. The appropriate columns and objects can be generated using the wrapper import functions `import_MaxQuant` and `import_IsobarQuant` or the generic functions `make_se` and `make_se_parse`.
`thr` | Integer(1), Sets the threshold for the allowed number of missing values per condition.
`fun` | "man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed" or "nbavg", Function used for data imputation based on `manual_impute` and `impute`.
`...` | Additional arguments for imputation functions as depicted in `manual_impute` and `impute`.

### Value

A filtered, normalized and imputed SummarizedExperiment object.

### Examples

    # Load datasets
    data <- UbiLength
    exp_design <- UbiLength_ExpDesign

    # Import data
    se <- import_MaxQuant(data, exp_design)

    # Process data
    processed <- process(se)

[Package DEP version 1.9.0 Index]