If you only have RNAseq results, you may want to try if GeneNetworkBuilder could build a network for differential expressed gene. Here is the sample code for that.
library(GeneNetworkBuilder)
try({ ## just in case STRINGdb not work
library(STRINGdb)
curr_version_table <-
read.table(url("https://string-db.org/api/tsv-no-header/version"),
colClasses = "character")$V1[1]
string_db <- STRINGdb$new( version=curr_version_table, species=9606,
score_threshold=400)
data(diff_exp_example1)
example1_mapped <- string_db$map( diff_exp_example1, "gene", removeUnmappedRows = TRUE )
i <- string_db$get_interactions(example1_mapped$STRING_id)
colnames(example1_mapped) <- c("gene", "P.Value", "logFC", "symbols")
## get significant up regulated genes.
genes <- unique(example1_mapped$symbols[example1_mapped$P.Value<0.005 & example1_mapped$logFC>3])
x<-networkFromGenes(genes = genes, interactionmap=i, level=3)
## filter network
## unique expression data by symbols column
expressionData <- uniqueExprsData(example1_mapped,
method = 'Max',
condenseName = "logFC")
## merge binding table with expression data by symbols column
cifNetwork<-filterNetwork(rootgene=x$rootgene,
sifNetwork=x$sifNetwork,
exprsData=expressionData, mergeBy="symbols",
miRNAlist=character(0),
tolerance=1, cutoffPVal=0.001, cutoffLFC=1)
## convert the id back to symbol
IDsMap <- expressionData$gene
names(IDsMap) <- expressionData$symbols
cifNetwork <- convertID(cifNetwork, IDsMap)
## add additional info for searching, any character content columns
cifNetwork$info1 <- sample(c("groupA", "groupB"),
size = nrow(cifNetwork),
replace = TRUE)
## polish network
gR<-polishNetwork(cifNetwork)
## browse network
browseNetwork(gR)
## try predifined colors
cifNetwork$color <- sample(rainbow(7), nrow(cifNetwork), replace = TRUE)
## polish network
gR<-polishNetwork(cifNetwork, nodecolor="color")
## browse network
browseNetwork(gR)
})
## Warning: we couldn't map to STRING 15% of your identifiers