Perform mixed imputation on a data matrix
Arguments
- data
A matrix with intensity values
- ed
The experimental design data frame.
- cutoff
The cutoff used for MAR/MNAR classification. See create_imputation_mask
Examples
se <- prepare_se(report.pg_matrix, expDesign, report.pr_matrix, impute = 'none')
data_missing <- as.matrix(SummarizedExperiment::assay(se)) # Intensity matrix with missing values
ed <- as.data.frame(SummarizedExperiment::colData(se)) # The experimental conditions
data_full <- mixed_imputation_matrix(data_missing, ed, cutoff = 'empirically')
#> Imputing along margin 2 (samples/columns).
#> [1] 0.3066195
#> Imputing along margin 1 (features/rows).
#> Warning: 36 rows with more than 50 % entries missing;
#> mean imputation used for these rows
#> Cluster size 5574 broken into 3662 1912
#> Cluster size 3662 broken into 1462 2200
#> Done cluster 1462
#> Cluster size 2200 broken into 1110 1090
#> Done cluster 1110
#> Done cluster 1090
#> Done cluster 2200
#> Done cluster 3662
#> Cluster size 1912 broken into 1379 533
#> Done cluster 1379
#> Done cluster 533
#> Done cluster 1912