In MarzyUnicz/MMRFBiolinks:

library(MMRFBiolinks)
library(TCGAbiolinks)

library(SummarizedExperiment)
library(dplyr)
library(DT)

Downloading and preparing data for analysis

Data download

MMRFBiolinks is an extension of [TCGAbiolinks package](https://bioconductor.org/packages/TCGAbiolinks/) which should be consulted. MMRFBiolinks relies on the usage of TCGABiolinks functions among which TCGAbiolinks::GDCdownload function to download MMRF-COMMPASS data available at GDC Data Portal.

Data prepare

When using the function MMRFBiolinks::MMRFGDC_prepare there is an argument called SummarizedExperiment which defines the output type Summarized Experiment (default option) or a data frame

Summarized Experiment

[SummarizedExperiment object](http://www.nature.com/nmeth/journal/v12/n2/fig_tab/nmeth.3252_F2.html) contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. A SummarizedExperiment object has three main matrices that can be accessed using the [SummarizedExperiment package](http://bioconductor.org/packages/SummarizedExperiment/)): - Sample matrix information is accessed via `colData(data)`: stores sample information and further indexed clinical data and subtype information; - Assay matrix information is accessed via `assay(data)`: stores molecular data: - Feature matrix information (gene information) is accessed via `rowRanges(data)`: stores metadata about the features, including their genomic ranges. MMRFGDC_prepare transforms the downloaded data into a summarizedExperiment object or a data frame. If summarizedExperiment is set to TRUE, metadata will add to the object. If a summarizedExperiment object was chosen, data can be accessed with three different accessors: assay for the data information, rowRanges to gets the range of values in each row and colData to get the sample information (patient, batch, sample type, etc) 8,9

Functions

`GDCdownload`

Uses GDC API or GDC transfer tool to download GDC data. MMRFBiolinks is an extension of TCGAbiolinks package which should be consulted.

`MMRFGDC_prepare`

Reads the data downloaded and prepare it into an R object.

| Argument | Description | |------------------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 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" |

Example:

library(DT)
query.mm<-GDCquery(project = "MMRF-COMMPASS",
                    data.category = "Transcriptome Profiling",
                    data.type = "Gene Expression Quantification",
                    workflow.type="HTSeq - FPKM",
                    barcode = c("MMRF_2473","MMRF_2111",
                                "MMRF_2362","MMRF_1824"))

GDCdownload(query.mm, method = "api", files.per.chunk = 10)
data <- MMRFGDC_prepare(query.mm)

data <- mm.exp.bar

library(SummarizedExperiment)


datatable(as.data.frame(colData(data)), 
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)

# Only first 100 to make faster
datatable(assay(data)[1:100,], 
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = TRUE)
rowRanges(data)