Function that allows for the imputation of missing values using 4 possible algorithms: missForest, kNN, LLS, SVD.
impute.counts(
DEprot.object,
method = "missForest",
use.normalized.data = TRUE,
overwrite.imputation = FALSE,
missForest.max.iterations = 100,
missForest.variable.wise.OOBerror = TRUE,
missForest.cores = 1,
missForest.parallel.mode = "variables",
kNN.n.nearest.neighbours = 10,
LLS.k = 2,
verbose = FALSE
)A DEprot object, as generated by load.counts.
String indicating the imputation method to use. One among: 'missForest', 'kNN', 'LLS', 'SVD'. Default: "missForest".
Logical value indicating whether the imputation should be performed based on the rationalized data. Default: TRUE.
Logical value to indicate whether, in the case already available, the table of imputed counts should be overwritten. Default: FALSE.
Max number of iterations for the missForest algorithm. Default: 100.
Logical value to define whether the OOB error is returned for each variable separately. Default: TRUE.
Number of cores used to run the missForest algorithm. If missForest.cores is 1 (or lower), the imputation will be run in parallel. Two modes are possible and can be defined by the parameter missForest.parallel.mode. Default: 1.
Define the mode to use for the parallelization, ignored when cores is more than 1. One among: 'variables', 'forests'. Default: "variables". See also the documentation of the missForest function.
Numeric value indicating the number of nearest neighbors to use to perform the kNN imputation. Default: 10.
Cluster size, this is the number of similar genes used for regression. Default: 2.
Logical valued indicating whether processing messages should be printed. Default: FALSE.
A DEprot object. The boxplot showing the distribution of the protein intensity is remade and added to the slot (boxplot.imputed). A list with parameters and other info about the imputation is added as well in the imputation slot.