imputedGLMnetwork: Multiple hot-deck imputation and network inference from...

Description Usage Arguments Details Value Author(s) References Examples

View source: R/main.R

Description

imputedGLMnetwork performs a multiple hot-deck imputation and infers a network for each imputed dataset with a log-linear Poisson graphical model (LLGM).

Usage

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imputedGLMnetwork(X, Y, sigma, m = 50, lambdas = NULL, B = 20)

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

affinity threshold for donor pool

m

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

lambdas

a sequence of decreasing positive numbers to control the regularization (numeric vector). Default to NULL

B

number of iterations for stability selection. Default to 20

Details

When input lambdas are null the default sequence of glmnet for the first model (the one with the first column of count as the target) is used. A common default sequence is generated for all imputed datasets using this method.

Value

S3 object of class HDpath: a list consisting of

Author(s)

Alyssa Imbert, alyssa.imbert@gmail.com

Nathalie Vialaneix, nathalie.vialaneix@inrae.fr

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. doi: 10.1093/bioinformatics/btx819.

Examples

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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)
lambdas <- 4 * 10^(seq(0, -2, length = 10))
## Not run: 
lung_hdmi <- imputedGLMnetwork(lung, thyroid, sigma = 2, lambdas = lambdas,
                               m = 10, B = 5)

## End(Not run)

RNAseqNet documentation built on July 2, 2020, 4:15 a.m.