RNAseqNet (version 0.1.5)

imputeHD: Impute missing row datasets with multiple hot deck.

Description

imputeHD performs multiple hot-deck imputation on an input data frame with missing rows. Each missing row is imputed with a unique donor. This method requires an auxiliary dataset to compute similaritities between individuals and create the pool of donors.

Usage

imputeHD(X, Y, sigma, m = 50, seed = NULL)

Value

S3 object of class HDImputed: a list consisting of

donors

a list. Each element of this list contains the donor pool for every missing observations

draws

a data frame which indicates which donor was chosen for each missing samples

data

a list of m imputed datasets

Arguments

X

n x p numeric matrix containing RNA-seq expression with missing rows (numeric matrix or data frame)

Y

auxiliary dataset (n' x q numeric matrix or data frame)

sigma

threshold for hot-deck imputation (numeric, positive)

m

number of replicates in multiple imputation (integer). Default to 50

seed

single value, interpreted as an in integer, used to initialize the random number generation state. Default to NULL (not used in this case)

Author

Alyssa Imbert, Nathalie Vialaneix,

Details

Missing values are identified by matching rownames in X and Y. If rownames are not provided the missing rows in X are supposed to correspond to the last rows of Y.

References

Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Lefebvre, G. Gourraud, P.A., Viguerie, N. and Villa-Vialaneix, N. (2018) Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics. tools:::Rd_expr_doi("10.1093/bioinformatics/btx819").

See Also

chooseSigma, imputedGLMnetwork

Examples

data(lung)
data(thyroid)
nobs <- nrow(lung)
miss_ind <- sample(1:nobs, round(0.2 * nobs), replace = FALSE)
lung[miss_ind, ] <- NA
lung <- na.omit(lung)
imputed_lung <- imputeHD(lung, thyroid, sigma = 2)