In marziasettino/MMRFBiolinks: R package for integrative analysis of MMRF-CoMMpass data

library(TCGAbiolinks)
library(MMRFBiolinks)
library(SummarizedExperiment)
library(dplyr)
library(DT)

MMRFGDC_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

Downloading and preparing data for analysis

MMRFGDC_prepare

MMRFGDC_prepare allows the user to prepare the gene expression data into an R object for futher analyses.The useful arguments for preparing data from MMRF-CoMMpass Project data are:

| 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:

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

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)

marziasettino/MMRFBiolinks documentation built on Jan. 24, 2023, 4:49 a.m.