MMRFprepare: Prepare GDC data

In MarzyUnicz/MMRFBiolinksX:

Description

Reads the data downloaded and prepare it into an R object

Usage

MMRFprepare(
  query,
  save = FALSE,
  save.filename,
  directory = "GDCdata",
  summarizedExperiment = TRUE,
  remove.files.prepared = FALSE,
  add.gistic2.mut = NULL,
  mut.pipeline = "mutect2",
  mutant_variant_classification = c("Frame_Shift_Del", "Frame_Shift_Ins",
    "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del",
    "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation")
)

Arguments

query	A query for GDCquery function
save	Save result as RData object?
save.filename	Name of the file to be save if empty an automatic will be created
directory	Directory/Folder where the data was downloaded. Default: GDCdata
summarizedExperiment	Create a summarizedExperiment? Default TRUE (if possible)
remove.files.prepared	Remove the files read? Default: FALSE This argument will be considered only if save argument is set to true
add.gistic2.mut	If a list of genes (gene symbol) is given, columns with gistic2 results from GDAC firehose (hg19) and a column indicating if there is or not mutation in that gene (hg38) (TRUE or FALSE - use the MAF file for more information) will be added to the sample matrix in the summarized Experiment object.
mut.pipeline	If add.gistic2.mut is not NULL this field will be taken in consideration. Four separate variant calling pipelines are implemented for GDC data harmonization. Options: muse, varscan2, somaticsniper, MuTect2. For more information: https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/
mutant_variant_classification	List of mutant_variant_classification that will be consider a sample mutant or not. Default: "Frame_Shift_Del", "Frame_Shift_Ins", "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del", "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation"

Value

A summarizedExperiment or a data.frame

Examples

## Not run: 
query.mm.fpkm <- GDCquery(project = "MMRF-COMMPASS",
                          data.category = "Transcriptome Profiling",
                          data.type = "Gene Expression Quantification",
                          workflow.type="HTSeq - FPKM")


GDCdownload(query.mm.fpkm, method = "api", files.per.chunk = 100)
DataGDC.prep <- GDCprepare(query.mm.fpkm,
                           save = TRUE ,
                           save.filename = "MMCompassFPKM.rda" ,
                           directory = "GDCdata" ,
                           summarizedExperiment = TRUE)

## End(Not run)

MarzyUnicz/MMRFBiolinksX documentation built on April 7, 2020, 1:05 p.m.