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Handle missing values

Source: R/handle_missing_values.R
handle_missing_values.Rd

Imputes missing values in an expression matrix using various methods, including simple replacement with the lowest non-zero value or more sophisticated imputation techniques via the MsCoreUtils package.

Usage

handle_missing_values(data, imputation_mode = "lowest_value", ...)

Arguments

data

A numeric matrix containing the data to be processed, with identifiers as row names and samples as columns.

imputation_mode

A string specifying the imputation method to use:

  • "lowest_value" (default): Replaces NAs with the lowest non-zero value in the matrix

  • "knn": k-nearest neighbor imputation

  • "zero": Replace NAs with zeros

  • "MLE": Maximum likelihood estimation

  • "bpca": Bayesian principal component analysis

  • "RF": Random Forest imputation

  • "min": Replace NAs with minimum value in each column

  • "MinDet": Deterministic minimum value imputation

  • "MinProb": Probabilistic minimum value imputation

  • "QRILC": Quantile regression imputation of left-censored data

  • "mixed": Mixed imputation based on feature-wise missingness

  • "nbavg": Impute with average of neighbors

...

Additional arguments passed to MsCoreUtils::impute_matrix. See the documentation of that function for method-specific parameters.

Value

A matrix with the same dimensions as the input, but with missing values imputed according to the specified method.

See also

impute_matrix for detailed description of the imputation methods

Examples

# Create example matrix with missing values
mat <- matrix(c(1.2, 3.4, NA, 5.6, NA, 7.8, 9.0, 2.1, 4.3), nrow = 3, ncol = 3)
rownames(mat) <- c("Protein1", "Protein2", "Protein3")
colnames(mat) <- c("Sample1", "Sample2", "Sample3")

# View original matrix
print(mat)
#>          Sample1 Sample2 Sample3
#> Protein1     1.2     5.6     9.0
#> Protein2     3.4      NA     2.1
#> Protein3      NA     7.8     4.3

# Impute missing values with lowest non-zero value (default)
result1 <- handle_missing_values(mat)
print(result1)
#>          Sample1 Sample2 Sample3
#> Protein1     1.2     5.6     9.0
#> Protein2     3.4     1.2     2.1
#> Protein3     1.2     7.8     4.3

# Impute missing values using k-nearest neighbors
if (FALSE) { # \dontrun{
# Requires the 'impute' package
result2 <- handle_missing_values(mat, imputation_mode = "knn")
print(result2)
} # }