inst/scripts/prepareRegulons.R

In federicogiorgi/aracne.networks: ARACNe-inferred gene networks from TCGA tumor datasets

load("C:/Users/fmg2117/Dropbox/Projects_CUMC/aracne.networks/regulons.Rda")

acronyms<-c(
    "blca","brca","cesc","coad","esca","gbm","hnsc","kirc","kirp",
    "laml","lihc","luad","lusc","ov","paad","pcpg","prad","read",
    "sarc","stad","tgct","thca","thym","ucec"
)

niceAcros<-c("Bladder Carcinoma","Breast Carcinoma",
             "Cervical Squamous Carcinoma",
             "Colon Adenocarcinoma","Esophageal Carcinoma","Glioblastoma",
             "Head and Neck Squamous Carcinoma",
             "Kidney Renal Clear Cell Carcinoma",
             "Kidney Papillary Carcinoma",
             "Acute Myeloid Leukemia","Liver Hepatocellular Carcinoma",
             "Lung Adenocarcinoma","Lung Squamous Carcinoma",
             "Ovarian Carcinoma","Pancreas Carcinoma",
             "Pheochromocytoma and Paraganglioma",
             "Prostate Carcinoma","Rectal Adenocarcinoma","Sarcoma",
             "Stomach Adenocarcinoma","Testicular Cancer",
             "Thyroid Carcinoma","Thymoma",
             "Utherine Corpus Endometroid Carcinoma")
names(niceAcros)<-acronyms


# Elegant solution
cat("",file="data/datalist")
for (acronym in acronyms){
    message("Doing ",acronym)
    a<-paste0("regulon",acronym,"<-regul$",acronym)
    b<-paste0("save(regulon",acronym,",file='data/regulon",acronym,".rda')")
    command<-paste0(a,";",b)
    eval(parse(text=command))

    cat(paste0("regulon",acronym,": regulon",acronym,"\n"),
        file="data/datalist",append=TRUE)
    #cat(paste0("regulon",acronym,"\n"),file="data/datalist",append=TRUE)
}



# Build a manual
for (acronym in acronyms){
    manstring<-paste0("
        \\name{regulon",acronym,"}
        \\Rdversion{1.4}
        \\alias{regulon",acronym,"}
        \\docType{data}
        \\title{Human ",niceAcros[acronym]," context-specific ARACNe interactome}
        \\description{
         The interactome is a human ",niceAcros[acronym]," context-specific
         regulatory network reverse engineered by the ARACNE-AP algorithm.
         The interactome is contained in a list object of S3 class `regulon'
         where each element represent a transcriptional
         regulator (transcription factor) and contains two vectors: (1) a named
         numeric vector indicating the mode of regulation (MoR)
         for each target gene, whose ID is indicated by the names attribute of
         the vector. (2) a numeric vector indicating the confidence score for
         the TF-target interaction.
         }
         \\value{
         Object of class regulon (\\link[viper]{regulon-class}) containing
        network data generated by ARACNe-AP.
         }

        \\usage{data(regulon",acronym,")}
        \\examples{
             data(regulon",acronym,")
             write.regulon(regulon",acronym,",n=10)
        }
        \\references{
        Giorgi,F.M. et al. (2016) ARACNe-AP: Gene Network Reverse Engineering through Adaptive Partitioning inference of Mutual Information. Bioinformatics doi: 10.1093/bioinformatics/btw216.
        }
        \\keyword{datasets}
    ")
    cat(manstring,file=paste0("man/regulon",acronym,".Rd"))
}