PomaImpute {POMA} | R Documentation

## Collettion of Imputation Methods for Mass Spectrometry Data

### Description

PomaImpute() offers different methods to impute missing values in MS data.

### Usage

    PomaImpute(
      data,
      ZerosAsNA = FALSE,
      RemoveNA = TRUE,
      cutoff = 20,
      method = "knn"
    )

### Arguments

`data` | A MSnSet object. First `pData` column must be the subject group/type.
`ZerosAsNA` | Logical that indicates if the zeros in the data are missing values. Default is FALSE.
`RemoveNA` | Logical that indicates if those features with more than selected cutoff missing values in each group have to be removed. Default is TRUE.
`cutoff` | Numeric that indicates the percentage of missing values allowed in each group. If one of the groups have less missing values than selected cutoff value, these feature will not be removed.
`method` | Imputation method. Options are: "none", "half_min", "median", "mean", "min", "knn" and "rf". If "none", all missing values will be replaced by zero.

### Value

A MSnSet object with cleaned data.

### Author(s)

Pol Castellano-Escuder

### References

Armitage, E. G., Godzien, J., Alonso‐Herranz, V., López‐Gonzálvez, Á., & Barbas, C. (2015). Missing value imputation strategies for metabolomics data. Electrophoresis, 36(24), 3050-3060.

### Examples

    data("st000336")

    PomaImpute(st000336, method = "knn")

[Package POMA version 1.0.0 Index]