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## ---- echo=TRUE,eval=FALSE----------------------------------------------------
# require(STRINGdb)
# require(igraph)
# require(biomaRt)
#
# # 1. getSTRINGdb for human
# string_db <- STRINGdb$new(species=9606)
# human_graph <- string_db$get_graph()
#
# # 2. get edges with high confidence score
# edge.scores <- E(human_graph)$combined_score
# ninetyth.percentile <- quantile(edge.scores, 0.9)
# thresh <- data.frame(name='90th percentile',
# val=ninetyth.percentile)
# human_graph <- subgraph.edges(human_graph,
# E(human_graph)[combined_score > ninetyth.percentile])
#
# # 3. create adjacency matrix
# adj_matrix <- as_adjacency_matrix(human_graph)
#
#
# # 4. map gene ids to protein ids
#
# ### get gene/protein ids via Biomart
# mart=useMart(host = 'grch37.ensembl.org',
# biomart='ENSEMBL_MART_ENSEMBL',
# dataset='hsapiens_gene_ensembl')
#
# ### extract protein ids from the human network
# protein_ids <- sapply(strsplit(rownames(adj_matrix), '\\.'),
# function(x) x[2])
#
# ### get protein to gene id mappings
# mart_results <- getBM(attributes = c("ensembl_gene_id",
# "ensembl_peptide_id"),
# filters = "ensembl_peptide_id", values = protein_ids,
# mart = mart)
#
# ### replace protein ids with gene ids
# ix <- match(protein_ids, mart_results$ensembl_peptide_id)
# ix <- ix[!is.na(ix)]
#
# newnames <- protein_ids
# newnames[match(mart_results[ix,'ensembl_peptide_id'], newnames)] <-
# mart_results[ix, 'ensembl_gene_id']
# rownames(adj_matrix) <- newnames
# colnames(adj_matrix) <- newnames
#
# ppi <- adj_matrix[!duplicated(newnames), !duplicated(newnames)]
# nullrows <- Matrix::rowSums(ppi)==0
# ppi <- ppi[!nullrows,!nullrows] ## ppi is the network with gene ids
## -----------------------------------------------------------------------------
sessionInfo()
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