| ppi.infer.human {PPInfer} | R Documentation |
This function is designed for human protein-protein interaction from STRING database. Default format is 'hgnc'. 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.
ppi.infer.human(target, kernel, top = 10, classifier = net.infer,
input = "hgnc_symbol", output = "hgnc_symbol", ...)
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 |
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 |
Dongmin Jung, Xijin Ge
net.infer, net.infer.ST, getBM
# example 1
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.human <- ppi.infer.human(target, K.9606, input = "ensembl_peptide_id")
## Not run:
# example 2
library(graph)
data(apopGraph)
target <- nodes(apopGraph)
apoptosis.infer <- ppi.infer.human(target, K.9606, 100)
# example 3
library(KEGGgraph)
library(KEGG.db)
pName <- "p53 signaling pathway"
pId <- mget(pName, KEGGPATHNAME2ID)[[1]]
getKGMLurl(pId, organism = "hsa")
p53 <- system.file("extdata/hsa04115.xml", package="KEGGgraph")
p53graph <- parseKGML2Graph(p53,expandGenes=TRUE)
entrez <- translateKEGGID2GeneID(nodes(p53graph))
httr::set_config(httr::config(ssl_verifypeer = FALSE))
human.ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")
target <- getBM(attributes=c('entrezgene', 'hgnc_symbol'),
filter = 'entrezgene', values = entrez,
mart = human.ensembl)[,2]
p53.infer <- ppi.infer.human(target, K.9606, 100)
## End(Not run)