---标题：“综合分析研讨会与TCGabiolinks和Elmer - 获取数据”作者：“Tiago Chedraoui Silva，Simon Coetzee，Dennis Hazelett，Ben Berman，Houtan Noushmehr”日期：“`r sys.date（）`”输出：html_document：self_contined：true number_sections：没有主题：andry突出显示：tango mathjax：null toc：true toc_float：true toc_float- 获取数据}％\ vignetteengine {knitr :: Rarmmardown}％\ vignetteencoding {utf-8} ---```{r，echo = false，hide = true，message = false，warning = false} devtools :: load_all（“。”）```＃介绍本节中，我们将学习从新创建的[NCI基因组数据公共（GDC）门户网站（HTTPS：）搜索和下载DNA甲基化（表观遗传学）和基因表达（转录）数据]（HTTPS：//portal.gdc.cancer.gov/）并准备将它们融入概述实验对象。下图突出显示本节将涵盖的工作流部分。！[本节中涵盖的工作流程的一部分]（图/ Workflow_Tgcabiolinks.png）＃下载数据##加载所需的库```{R libs，eval = true，message = false，warning = f}库（tcgabiolinks）库（概述）库（DT）库（DT）库（DT）```##基因表达式```{R TCGabiolinks-exp，eval = false} query.exp < - gdcquery（project =“tcga-lusc”，data.category =“转录组分析”，data.type =“基因表达量化”，workflow.type =“htseq - fpkm-uq”，条形码= c（“Tcga-34-5231-01”，“TCGA-77-7138-01”））gdcdownload（query.exp）exp < - gdcprepare（query = query.exp，save = true，save.filename =“exp_lusc.rda”，summarizedexperiment = true）`````{r tcgabiolinks-exp-objj，eval = true} exp coldata（exp）％>％as.data.frame％>％datapable（options = list（scrollx = true），rownames = true）测定（exp）[1：5，]％>％datapable（选项=列表（Scrollx = True），Rownames = True）ROWRANGES（EXP）````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````ICoctor包[TcGabiolinks]（http：//biocondion.org/packages/tcgabiolinks/）[@tcgabiolinks]从[nci基因组数据公共（gdc）portal]（https://portal.gdc.cancer.gov/）。 In this example, we will download DNA methylation data (Infinium HumanMethylation450 platform) for two TCGA-LUSC (TCGA Lung Squamous Cell Carcinoma) samples. GDCquery function will search in the GDC database for the information required to download the data, this information is used by the `GDCdownload` function which will request the files to GDC, those files will be compacted into a 76 MB tar.gz file. After the download is completed `GDCdownload` will uncompress the tar.gz file and move its files to a folder; the default is GDCData/(Project)/(source)/(data.category)/(data.type)), in our example, it will be `GDCdata/TCGA-LUSC/harmonized/DNA_Methylation/Methylation_Beta_Value/`  Finally, `GDCprepare` transforms the downloaded data into a [summarizedExperiment](//www.andersvercelli.com/packages/SummarizedExperiment/) object [@huber2015orchestrating] or a data frame. If *SummarizedExperiment* is set to TRUE, TCGAbiolinks will add to the object molecular sub-type information, which was defined by The Cancer Genome Atlas (TCGA) Research Network reports (the full list of papers can be seen in [TCGAquery\_subtype section](//www.andersvercelli.com/packages/devel/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html#tcgaquery_subtype-working-with-molecular-subtypes-data.) in TCGAbiolinks vignette), and clinical information. ```{r tcgabiolinks-met, eval=FALSE} query.met <- GDCquery(project = "TCGA-LUSC", data.category = "DNA Methylation", platform = "Illumina Human Methylation 450", barcode = c("TCGA-34-5231-01A-21D-1818-05","TCGA-77-7138-01A-41D-2043-05")) GDCdownload(query.met) met <- GDCprepare(query = query.met, save = TRUE, save.filename = "DNAmethylation_LUSC.rda", summarizedExperiment = TRUE) ``` The object created is a Sum ```{r tcgabiolinks-met-obj, eval=TRUE} met colData(met) %>% as.data.frame %>% datatable(options = list(scrollX = TRUE), rownames = TRUE) assay(met)[1:5,] %>% datatable (options = list(scrollX = TRUE), rownames = TRUE) rowRanges(met) ``` # Session Info ```{r sessioninfo, eval=TRUE} sessionInfo() ``` # Bibliography