ppi.infer.mouse: Inferring functionally related proteins using protein...

In dongminjung/PPInfer: Inferring functionally related proteins using protein interaction networks

Description

This function is designed for mouse protein-protein interaction from STRING database. Default format is 'mgi'. The number of proteins is 10 in default. Note that the number of proteins used as a target may be different from the number of proteins in the input since mapping between formats is not always one-to-one in getBM.

Usage

ppi.infer.mouse(target, kernel, top = 10, classifier = net.infer,
                input = "mgi_symbol", output = "mgi_symbol", ...)

Arguments

target	set of interesting proteins or target class
kernel	the regularized Laplacian matrix for a graph
top	number of top proteins most closely related to target class (default: 10)
classifier	net.infer or net.infer.ST (default: net.infer)
input	input format
output	output format
...	additional parameters for the chosen classifier

Value

list	list of a target class used in the model
error	training error
CVerror	cross validation error, (when cross > 0 in net.infer)
top	top proteins
score	decision values for top proteins

Author(s)

Dongmin Jung, Xijin Ge

See Also

net.infer, net.infer.ST, getBM

Examples

string.db.10090 <- STRINGdb$new(version = '10', species = 10090,
                                score_threshold = 999)
string.db.10090.graph <- string.db.10090$get_graph()
K.10090 <- net.kernel(string.db.10090.graph)
rownames(K.10090) <- substring(rownames(K.10090), 7)
colnames(K.10090) <- substring(colnames(K.10090), 7)
target <- colnames(K.10090)[1:100]
infer.mouse <- ppi.infer.mouse(target, K.10090, input="ensembl_peptide_id")

dongminjung/PPInfer documentation built on May 15, 2019, 10:41 a.m.