Description Usage Arguments Format Examples
The impute
method performs data imputation on QFeatures
and
SummarizedExperiment
instance using a variety of methods.
Users should proceed with care when imputing data and take precautions to assure that the imputation produce valid results, in particular with naive imputations such as replacing missing values with 0.
See MsCoreUtils::impute_matrix()
for details on
the different imputation methods available and strategies.
1 2 3 4 5 6 7 |
object |
A |
method |
|
... |
Additional parameters passed to the inner imputation
function. See |
i |
Defines which element of the |
An object of class standardGeneric
of length 1.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | MsCoreUtils::imputeMethods()
data(se_na2)
## table of missing values along the rows (proteins)
table(rowData(se_na2)$nNA)
## table of missing values along the columns (samples)
colData(se_na2)$nNA
## non-random missing values
notna <- which(!rowData(se_na2)$randna)
length(notna)
notna
impute(se_na2, method = "min")
if (require("imputeLCMD")) {
impute(se_na2, method = "QRILC")
impute(se_na2, method = "MinDet")
}
if (require("norm"))
impute(se_na2, method = "MLE")
impute(se_na2, method = "mixed",
randna = rowData(se_na2)$randna,
mar = "knn", mnar = "QRILC")
## neighbour averaging
x <- se_na2[1:4, 1:6]
assay(x)[1, 1] <- NA ## min value
assay(x)[2, 3] <- NA ## average
assay(x)[3, 1:2] <- NA ## min value and average
## 4th row: no imputation
assay(x)
assay(impute(x, "nbavg"))
|
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