mv_imputation {pmp} | R Documentation

## Missing value imputation using different algorithms

### Description

Missing values in metabolomics data sets occur widely and can originate from a number of sources, including technical and biological reasons.
Missing values imputation is applied to replace non-existing values with an estimated values while maintaining the data structure. A number of different methods are available as part of this function.

### Usage

    mv_imputation(
      df,
      method,
      k = 10,
      rowmax = 0.5,
      colmax = 0.5,
      maxp = NULL,
      check_df = TRUE
    )

### Arguments

`df` | A matrix-like (e.g. an ordinary matrix, a data frame) or RangedSummarizedExperiment-class object with all values of class `numeric()` or `integer()` of peak intensities, areas or other quantitative characteristic.
`method` | `character(1)`, missing value imputation method. Supported methods are `knn`, `rf`, `bpca`, `sv`, `'mn'` and `'md'`.
`k` | `numeric(1)`, for a given sample containing a missing value, the number of nearest neighbours to include to calculate a replacement value. Used only for method `knn`.
`rowmax` | `numeric(1)`, the maximum percentage of missing data allowed in any row. For any rows exceeding given limit, missing values are imputed using the overall mean per sample. Used only for method `knn`.
`colmax` | `numeric(1)`, the maximum percent missing data allowed in any column. If any column exceeds given limit, the function will report an error Used only for method `knn`.
`maxp` | `integer(1)`, number of features to run on single core. If set to NULL will use total number of features.
`check_df` | `logical(1)`, if set to TRUE will check if input data needs to be transposed, so that features are in rows.

### Details

Supported missing value imputation methods are:
`knn` - K-nearest neighbour. For each feature in each sample, missing values are replaced by the mean average value (non-weighted) calculated from its `k` closest neighbours in multivariate space (default distance metric: euclidean distance);
`rf` - Random Forest. This method is a wrapper of missForest function. For each feature, missing values are iteratively imputed until a maximum number of iterations (10), or until the difference between consecutively-imputed matrices becomes positive. Trees per forest are set to 100, variables included per tree are calculate using formula sqrt(total number of variables);
`bpca` - Bayesian principal component analysis. This method is a wrapper of pca function. Missing values are replaced by the values obtained from principal component analysis regression with a Bayesian method. Therefore every imputed missing value does not occur multiple times, neither across the samples nor across the metabolite features;
`sv` - Small value. For each feature, replace missing values with half of the lowest value recorded in the entire data matrix;
`'mn'` - Mean. For each feature, replace missing values with the mean average (non-weighted) of all other non-missing values for that variable;
`'md'` - Median. For each feature, replace missing values with the median of all other non-missing values for that variable.

### Value

Object of class `SummarizedExperiment`. If input data are a matrix-like (e.g. an ordinary matrix, a data frame) object, function returns the same R data structure as input with all value of data type `numeric()`.

### Examples

    df <- MTBLS79[ ,MTBLS79$Batch == 1]
    out <- mv_imputation(df=df, method='knn')

[Package pmp version 1.0.0 Index]