# mut

    library(oncoPredict)

    #This vignette demonstrates how to prepare predicted drug response and mutation
    #data for mutation-based IDWAS with idwas(cnv=FALSE).

    #Determine the parameters of the idwas() function...
    #Set the drug_prediction parameter.
    #Make sure rownames() are samples, and colnames() are drugs. Also make sure this data is a data frame.
    drug_prediction<-as.data.frame(read.table(vignette_file("DrugPredictions.txt"), header=TRUE, row.names=1))
    #In this example, replace '.' with '-' so the TCGA sample identifiers match the
    #format used in the mutation data.
    colnames(drug_prediction)<-gsub(".", "-", colnames(drug_prediction), fixed=T)
    #Make sure the sample identifiers in the 'drug prediction' data are of similar form as the sample identifiers in the 'data' parameter.
    cols=colnames(drug_prediction)
    colnames(drug_prediction)<-substring(cols, 3, nchar(cols))
    drug_prediction<-as.data.frame(t(drug_prediction))

This vignette provides an example of how to prepare mutation data from the GDC database for GBM (glioblastoma) and how to apply `idwas()` to test predicted drug response against somatic mutations.

Because GDC and TCGAbiolinks access patterns can change over time, the download code is shown as non-executed guidance.

Download mutation data for your cancer of interest from GDC database.

https://bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/mutation.html

https://rdrr.io/bioc/TCGAbiolinks/f/vignettes/mutation.Rmd

The code would look something like this:

    library(TCGAbiolinks)

    query_maf <- GDCquery(project = "TCGA-GBM",
                          data.category = "Simple Nucleotide Variation",
                          access = "open",
                          data.type = "Simple somatic mutation",
                          legacy = TRUE)

    GDCdownload(query_maf)
    maf <- GDCprepare(query_maf)

After downloading the mutation data, format the mutation table before running IDWAS.

    #Make sure this data is a data frame with mutation annotations in columns.
    #For idwas(cnv=FALSE), the data should include Variant_Classification,
    #Hugo_Symbol, and Tumor_Sample_Barcode.
    data<-as.data.frame(maf)
    samps<-data$Tumor_Sample_Barcode
    data$Tumor_Sample_Barcode<-substr(samps,1,nchar(samps)-12) #Make sure these sample ids are of the same form as the sample ids in your prediction data.

    #Determine the number of samples you want mutations to occur in. The default is 10.
    n=10

    #Indicate whether or not you would like to test CNA amplification data. If TRUE, you will test CNA amplifications. If FALSE, you will test mutation data.
    cnv=FALSE