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Impute missing values

Source: R/impute_groups.R
impute_groups.Rd

Impute missing values for each group of samples in a list of SummarizedExperiment objects. By default, replaces NA with the minimum non-NA value per group / subject. A custom function can be supplied for other strategies.

The input samples can contain replicates, or merged replicates (mean of replicates).

Usage

impute_groups(se_obj_list, fun = min, impute_by = c("group", "subject"))

Arguments

se_obj_list

A list of SummarizedExperiment objects, created by split_groups() or merge_replicates(), each corresponds to one group of samples.

fun

Function applied to the non-NA values to generate the replacement value. Default: min. Use function(x) min(x) / 2 for half-minimum, median for median imputation, etc.

impute_by

"group" (default): compute replacement value from all non-NA values in the group. "subject": compute per-subject replacement value (requires Subject column). If a subject is all NA, fall back to group-level replacement.

Value

A list of SummarizedExperiment objects with missing values imputed. Each object corresponds to one group of samples.

Examples

data(example_obj)
example_obj <- normalise_to_start(example_obj)
#> Normalising to group baseline at each feature's first non-NA time point.
example_obj_list <- split_groups(example_obj)
example_obj_merged_list <- merge_replicates(example_obj_list)
example_obj_merged_imp_list <- impute_groups(example_obj_merged_list)
#> Group IFNbeta: no missing values.
#> Group IFNgamma: no missing values.
#> Group LPS: no missing values.
#> Group untreated: no missing values.