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Missing Value Imputation using half minimum value

Source: R/Processing.R
mvi_imputation.Rd

Missing Value Imputation using half minimum value

Usage

mvi_imputation(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  mvi_percentage = 50
)

Arguments

data

DF which contains unique sample identifiers as row names and metabolite numerical values in columns with metabolite identifiers as column names. Use NA for metabolites that were not detected and consider converting any zeros to NA unless they are true zeros.

metadata_sample

DF which contains information about the samples, which will be combined with the input data based on the unique sample identifiers used as rownames.

metadata_info

Named vector containing the information about the names of the experimental parameters. c(Conditions="ColumnName_Plot_SettingsFile", Biological_Replicates="ColumnName_Plot_SettingsFile", core_media = "Columnname_Input_SettingsFile"). Column "Conditions" with information about the sample conditions, Column "BiologicalReplicates" including numerical values and Column "Columnname_Input_SettingsFile" is used to specify the name of the media controls in the Conditions.

core

Optional: If TRUE, a consumption-release experiment has been performed. Should not be normalised to media blank. Provide information about control media sample names via metadata_info "core_media" samples.Default = FALSE

mvi_percentage

Optional: percentage 0-100 of imputed value based on the minimum value. Default = 50

Value

DF with imputed values

This function can be used on raw or already filtered data, provided sample metadata and condition information are available.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- mvi_imputation(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    )
)
#> Missing Value Imputation: Missing value imputation is performed, as a complementary approach to address the missing value problem, where the missing values are imputing using the `half minimum value`. REF: Wei et. al., (2018), Reports, 8, 663, doi:https://doi.org/10.1038/s41598-017-19120-0
#> Some features had all NA values in all samples of certain conditions - hence no imputation was done and NA remains for: SAICAR