data_process

This function processes the abundance data based on user options, including removing features with missing values, imputing missing values, and normalization.

data_process(
  se,
  exclude_missing = TRUE,
  exclude_missing_pct = 70,
  replace_na_method = c("none", "QRILC", "SVD", "KNN", "IRMI", "min", "mean", "median",
    "PPCA", "BPCA", "RandomForest"),
  replace_na_method_ref = 0.5,
  normalization = c("none", "Percentage", "PQN", "Quantile", "Sum", "Median")
)

Arguments

se

A SummarizedExperiment object construct by as_summarized_experiment.

exclude_missing

Logical. If exclude_missing=TURE, lipids with missing values will be removed. Default is TRUE.

exclude_missing_pct

Numeric. Lipids with missing values over a certain percentage (5-100) should be removed. Default is 70.

replace_na_method

Character. The method for NA values replacing. Allowed methods include "QRILC", "SVD", "KNN", "IRMI", "min", "mean", "median", "PPCA", "BPCA", "RandomForest", and "none". If you have already replaced NAs, select 'none'. Default is 'min'.

replace_na_method_ref

Numeric. The value for replacing NA values varies depending on the selected method, and each method applies different number ranges.

1. QRILC: 0.1-1

2. SVD: 1-10

3. KNN: 1-10

4. min: 0.1-0.5

5. PPCA: 1-10

6. BPCA: 1-10

Default is 0.5 for replace_na_method='min'.

normalization

Character. Normalization function. Allowed methods include "Percentage", "PQN", "Quantile", "Sum", "Median", and "none". If you have already normalized the abundance values, select 'none'. Default is 'Percentage'.

Value

Return a SummarizedExperiment object with processed abundance values.

Examples

data("de_data_twoGroup")
processed_data <- data_process(de_data_twoGroup, exclude_missing=TRUE,
    exclude_missing_pct=70, replace_na_method='min', replace_na_method_ref=0.5,
    normalization='Percentage')