net.infer: Inferring functionally related proteins using networks

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

Description

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

Usage

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

# example 1
## Not run: 
string.db.9606 <- STRINGdb$new(version = '10',species = 9606,
                               score_threshold = 999)
string.db.9606.graph <- string.db.9606$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)

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