net.infer: Inferring functionally related proteins using networks

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/net.infer.R

Description

Proteins can be classified by using networks to identify functionally closely related proteins.

Usage

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net.infer(target, kernel, top = NULL, cross = 0,
          C = 1, nu = 0.2, epsilon = 0.1, cache1 = 40,
          tol1 = 0.001, shrinking1 = TRUE, cache2 = 40,
          tol2 = 0.001, shrinking2 = TRUE)

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: all proteins except for target and pseudo-absence class)

cross

if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model

C

cost of constraints violation for SVM (default: 1)

nu

The nu parameter for OCSVM (default: 0.2)

epsilon

epsilon in the insensitive-loss function for OCSVM (default: 0.1)

cache1

cache memory in MB for OCSVM (default: 40)

tol1

tolerance of termination criterion for OCSVM (default: 0.001)

shrinking1

option whether to use the shrinking-heuristics for OCSVM (default: TRUE)

cache2

cache memory in MB for SVM (default: 40)

tol2

tolerance of termination criterion for SVM (default: 0.001)

shrinking2

option whether to use the shrinking-heuristics for SVM (default: TRUE)

Value

list

list of a target class used in the model

error

training error

CVerror

cross validation error, (when cross > 0)

top

top proteins

score

decision values for top proteins

Author(s)

Dongmin Jung, Xijin Ge

References

Senay, S. D. et al. (2013). Novel three-step pseudo-absence selection technique for improved species distribution modelling. PLOS ONE. 8(8), e71218.

See Also

ksvm

Examples

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# example 1
## Not run: 
string.db.9606 <- STRINGdb$new(version = '11', species = 9606,
                               score_threshold = 999)
string.db.9606.graph <- string.db.9606$get_graph()
K.9606 <- net.kernel(string.db.9606.graph)
rownames(K.9606) <- substring(rownames(K.9606), 6)
colnames(K.9606) <- substring(colnames(K.9606), 6)
target <- colnames(K.9606)[1:100]
infer <- net.infer(target, K.9606, 10)

## End(Not run)

# example 2
data(litG)
litG <- igraph.from.graphNEL(litG)
sg <- decompose(litG, min.vertices = 50)
sg <- sg[[1]]
K <- net.kernel(sg)
litG.infer <- net.infer(names(V(sg))[1:10], K, top=20)

PPInfer documentation built on Nov. 8, 2020, 7:52 p.m.