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

Description Usage Arguments Value Author(s) See Also Examples

View source: R/ppi.infer.mouse.R

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

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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

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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")

PPInfer documentation built on Dec. 20, 2018, 6 p.m.