MS Metabolomics Data Processing Utilities

Description Usage Arguments Value References See Also Examples

Apply univariate imputation to a matrix or poplin object. The supported methods include

Half-minimum imputation: for each feature, missing values are replaced with half the observed minimum.
Median imputation: for each feature, missing values are replaced with the median of non-missing values.
Mean imputation: for each feature, missing values are replaced with the mean of non-missing values.

## S4 method for signature 'matrix'
impute_univariate(x, type = c("halfmin", "median", "mean"))

## S4 method for signature 'poplin'
impute_univariate(x, xin, xout, type = c("halfmin", "median", "mean"))

`x`	A matrix or poplin object.
`type`	Character specifying the method for univariate imputation.
`xin`	Character specifying the name of data to retrieve from `x` when `x` is a poplin object.
`xout`	Character specifying the name of data to store in `x` when `x` is a poplin object.

A matrix or poplin object of the same dimension as x containing the imputed intensities.

Wei, R., Wang, J., Su, M. et al. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Sci Rep 8, 663 (2018). https://doi.org/10.1038/s41598-017-19120-0

Other imputation methods: impute_knn(), impute_pca(), impute_randomforest(), poplin_impute()

data(faahko_poplin)

## poplin object
impute_univariate(faahko_poplin, xin = "raw", xout = "halfmin",
                  type = "halfmin")
## matrix
m <- poplin_raw(faahko_poplin, "raw")
impute_univariate(m, type = "median")