createStatisticalAdjacencyList {MetNet} | R Documentation |
The function infers adjacency matrix topologies from
statistical methods and returns matrices of these networks in a list. The
function includes functionality to caluclate adjacency matrices based on
LASSO (L1 norm)-regression, random forests, context likelihood of
relatedness (CLR), the algorithm for the reconstruction of accurate
cellular networks (ARACNE), Pearson correlation (also partial and
semipartial), Spearman correlation (also partial and semipartial)
and Constraint-based structure learning (Bayes). The function returns a
list of adjacency matrices that are defined by model
.
createStatisticalAdjacencyList(x, model, ...)
x |
matrix that contains intensity values of features/metabolites (rows) per sample (columns). |
model, |
character vector containing the methods that will be used ("lasso", "randomForest", "clr", "aracne", "pearson", "pearson_partial", "pearson_semipartial","spearman", "spearman_partial", "spearman_semipartial", "bayes") |
... |
parameters passed to the functions |
createStatisticalAdjacencyList
calls the function
lasso
, randomForest
, clr
, aracne
,
correlation
(for "pearson", "pearson_partial", "pearson_semipartial",
"spearman", "spearman_partial", "spearman_semipartial") and/or bayes
as specified by model
. It will create adjacency matrices using the
specified methods and will return a list containing the unweighted
adjacency matrix (if model
is of length 1) or append these
unweighted adjacency matrices to a list (if model
is of
length > 1). Internally x
will be z-scaled and the z-scaled object
will be used in lasso
, clr
and/or aracne
.
list containing the respective adjacency matrices specified by
model
Thomas Naake, thomasnaake@googlemail.com
data("x_test", package="MetNet") x <- x_test[, 3:dim(x_test)[2]] x <- as.matrix(x) createStatisticalAdjacencyList(x, c("pearson", "spearman"))