Impute missing values from bootstrapped subsampling

impute_missing(E, data, nk)

## Arguments

E |
4D array of clusterings from `consensus_cluster` . The number of rows
is equal to the number of cases to be clustered, number of columns is equal
to the clusterings obtained by different resamplings of the data, the third
dimension are the different algorithms and the fourth dimension are cluster
sizes. |

data |
data matrix with samples as rows and genes/features as columns |

nk |
cluster size to extract data for (single value) |

## Value

If flattened matrix consists of more than one repetition, i.e. it
isn't a column vector, then the function returns a matrix of clusterings
with complete cases imputed using majority voting, and relabelled, for
chosen `k`

.

## Details

The default output from `consensus_cluster`

will undoubtedly contain `NA`

entries because each replicate chooses a random subset (with replacement) of
all samples. Missing values should first be imputed using `impute_knn()`

. Not
all missing values are guaranteed to be imputed by KNN. See `class::knn()`

for details. Thus, any remaining missing values are imputed using majority
voting.

## See also

## Author

Aline Talhouk

## Examples

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