impute {DEP} | R Documentation

## Impute missing values

### Description

`impute` imputes missing values in a proteomics dataset.

### Usage

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

### Arguments

`se` | SummarizedExperiment, Proteomics data (output from `make_se()` or `make_se_parse()`). It is adviced to first remove proteins with too many missing values using `filter_missval()` and normalize the data using `normalize_vsn()`.
`fun` | "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "man", "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

An imputed SummarizedExperiment object.

### Examples

    # Load example
    data <- UbiLength
    data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",]
    data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

    # Make SummarizedExperiment
    columns <- grep("LFQ.", colnames(data_unique))
    exp_design <- UbiLength_ExpDesign
    se <- make_se(data_unique, columns, exp_design)

    # Filter and normalize
    filt <- filter_missval(se, thr = 0)
    norm <- normalize_vsn(filt)

    # Impute missing values using different functions
    imputed_MinProb <- impute(norm, fun = "MinProb", q = 0.05)
    imputed_QRILC <- impute(norm, fun = "QRILC")

    imputed_knn <- impute(norm, fun = "knn", k = 10, rowmax = 0.9)
    imputed_MLE <- impute(norm, fun = "MLE")

    imputed_manual <- impute(norm, fun = "man", shift = 1.8, scale = 0.3)

[Package DEP version 1.8.0 Index]