PomaImpute: Collettion of Imputation Methods for Mass Spectrometry Data

In POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of 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")

POMA documentation built on Nov. 8, 2020, 6:26 p.m.