Description Usage Arguments Details Value Author(s) References Examples
imputedGLMnetwork
performs a multiple hot-deck imputation and infers a
network for each imputed dataset with a log-linear Poisson graphical model
(LLGM).
1 | imputedGLMnetwork(X, Y, sigma, m = 50, lambdas = NULL, B = 20)
|
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 |
B |
number of iterations for stability selection. Default to 20 |
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.
S3 object of class HDpath
: a list consisting of
path
a list of m
data frames, each containing the
adjacency matrix of the inferred network obtained from the corresonding
imputed dataset. The regularization parameter is selected by StARS
efreq
a numeric matrix of size p x p, which indicates the
number of times an edge has been predicted among the m
inferred
networks
Alyssa Imbert, alyssa.imbert@gmail.com
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr
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.
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|
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