PomaImpute | R Documentation |
PomaImpute
performs missing value imputation on a dataset using various imputation methods.
PomaImpute(
data,
zeros_as_na = FALSE,
remove_na = TRUE,
cutoff = 20,
group_by = NULL,
method = "knn"
)
data |
A |
zeros_as_na |
Logical. Indicates if the zeros in the data are missing values. Default is FALSE. |
remove_na |
Logical. Indicates if features with a percentage of missing values over the |
cutoff |
Numeric. Percentage of missing values allowed in each feature. |
group_by |
Character. Indicates the name of the |
method |
Character. The imputation method to use. Options include "none" (no imputation, replace missing values by zeros), "half_min" (replace missing values with half of the minimum value), "median" (replace missing values with the median), "mean" (replace missing values with the mean), "min" (replace missing values with the minimum value), "knn" (replace missing values using k-nearest neighbors imputation), and "random_forest" (replace missing values using random forest imputation). |
A SummarizedExperiment
object without missing values.
Pol Castellano-Escuder
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.
# Output is a imputed SummarizedExperiment object
data <- POMA::st000284 # Example SummarizedExperiment object included in POMA
# No sample normalization
data %>%
PomaImpute(zeros_as_na = FALSE,
remove_na = TRUE,
cutoff = 20,
group_by = NULL,
method = "knn")
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