# The useage of GeoTcgaData

## Authors

Erqiang Hu

College of Bioinformatics Science and Technology, Harbin Medical University

## Installation

Get the released version from CRAN:

    install.packages("GeoTcgaData")

Or the development version from github:

    if(!requireNamespace("devtools", quietly = TRUE))
        install.packages("devtools")
    devtools::install_github("huerqiang/GeoTcgaData")

## Introduction

GEO and TCGA provide us with a wealth of data, such as RNA-seq, DNA Methylation, single nucleotide Variation and Copy number variation data. It’s easy to download data from TCGA using the gdc tool or `TCGAbiolinks`, and some software provides organized TCGA data, such as UCSC Xena，but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

    library(GeoTcgaData)
    #>  =============================================================
    #>  Hello, friend! welcome to use GeoTcgaData!
    #>  -------------------------------------------------------------
    #>  Version:1.0.2
    #>  =============================================================

## Common operations on GeoTcgaData

This is a basic example which shows you how to solve a common problem:

## RNA-seq data integration and differential gene extraction

It is convenient to use `TCGAbiolinks` or `GDCRNATools` to download and analysis Gene expression data. `TCGAbiolinks` use `edgeR` package to do differential expression analysis, while `GDCRNATools` can implement three most commonly used methods: limma, edgeR , and DESeq2 to identify differentially expressed genes (DEGs).

However, unlike the chip data, the RNA-seq data had one bias: the longer the gene, the more likely it was to be identified as a differential gene, while there was no such trend in the chip data. This is because in RNA-seq data a long gene has more reads mapping to it compared to a short gene of similar expression, and most of the statistical methods used to detect differential expression have stronger detection ability for genes with more reads. Therefore, we need to correct the gene length bias in downstream analysis such as enrichment analysis.

GOseq based on Wallenius’ noncentral hypergeometric distribution can effectively correct the gene length deviation in enrichment analysis. However, its algorithm can not directly correct the deviation of the expression profile, and its results can not be used for GSEA enrichment analysis. CQN present a normalization algorithm to correct systematic biases(gene length bias and GC-content bias), whose result can be seamlessly docked with downstream difference analysis software such as DESeq2 and edgeR.

Here we use `TCGAbiolinks` to download RNA-seq data, use `CQN` to correct gene length bias and GC content bias, and then use `DESeq2` for difference analysis.

use `TCGAbiolinks` to download TCGA data

    # download RNA-seq data
    library(TCGAbiolinks)

    query <- GDCquery(project = "TCGA-ACC",
                      data.category = "Transcriptome Profiling",
                      data.type = "Gene Expression Quantification",
                      workflow.type = "HTSeq - Counts")

    GDCdownload(query, method = "api", files.per.chunk = 3,
        directory = Your_Path)

    dataRNA <- GDCprepare(query = query, directory = Your_Path,
                          save = TRUE, save.filename = "dataRNA.RData")
    ## get raw count matrix
    dataPrep <- TCGAanalyze_Preprocessing(object = dataRNA,
                                          cor.cut = 0.6,
                                          datatype = "HTSeq - Counts")

Use `diff_RNA` to do difference analysis. We provide the data of human gene length and GC content in `gene_cov`.

    group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE)
    library(cqn) # To avoid reporting errors: there is no function "rq"
    ## get gene length and GC content
    library(org.Hs.eg.db)
    genes_bitr <- bitr(rownames(gene_cov), fromType = "ENTREZID", toType = "ENSEMBL",
             OrgDb = org.Hs.eg.db, drop = TRUE)
    genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ]
    gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
    rownames(gene_cov2) <- genes_bitr$ENSEMBL
    genes <- intersect(rownames(dataPrep), rownames(gene_cov2))
    dataPrep <- dataPrep[genes, ]
    geneLength <- gene_cov2[genes, "length"]
    gccontent <- gene_cov2[genes, "GC"]
    names(geneLength) <- names(gccontent) <- genes
    ##  Difference analysis
    DEGAll <- diff_RNA(counts = dataPrep, group = group,
                       geneLength = geneLength, gccontent = gccontent)

Use `clusterProfiler` to do enrichment analytics:

    diffGenes <- DEGAll$logFC
    names(diffGenes) <- rownames(DEGAll)
    diffGenes <- sort(diffGenes, decreasing = TRUE)
    library(clusterProfiler)
    library(enrichplot)
    library(org.Hs.eg.db)
    gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
    dotplot(gsego)

## DNA Methylation data integration

use `TCGAbiolinks` to download TCGA data

    library(TCGAbiolinks)
    query <- GDCquery(project = "TCGA-ACC",
                      data.category = "DNA Methylation",
                      data.type = "Methylation Beta Value",
                      platform = "Illumina Human Methylation 450")
    GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)

The function Merge_methy_tcga could Merge methylation data downloaded from TCGA official website or TCGAbiolinks. This makes it easier to extract differentially methylated genes in the downstream analysis. For example:

    merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data)

Then use `ChAMP` package to do difference analysis.

    library(ChAMP)
    diff_gene <- methyDiff(cpgData = merge_result, sampleGroup = sample(c("C","T"),
        ncol(merge_result[[1]]), replace = TRUE))

Use `clusterProfiler` to do enrichment analytics:

    diff_gene$p.adj <- p.adjust(diff_gene$pvalue)
    genes <- diff_gene[diff_gene$p.adj < 0.05, "gene"]
    library(clusterProfiler)
    library(enrichplot)
    library(org.Hs.eg.db)
    ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "SYMBOL")
    dotplot(ego)

## Copy number variation data integration and differential gene extraction

use TCGAbiolinks to download TCGA data(Gene Level Copy Number Scores)

    library(TCGAbiolinks)
    query <- GDCquery(project = "TCGA-LGG",
                      data.category = "Copy Number Variation",
                      data.type = "Gene Level Copy Number Scores")

    GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)

    data <- GDCprepare(query = query,
                       directory =  "Your_Path")

Do difference analysis of gene level copy number variation data using `diff_CNV`

    class(data) <- "data.frame"
    cnvData <- data[, -c(1,2,3)]
    rownames(cnvData) <- data[, 1]
    sampleGroup  = sample(c("A","B"), ncol(cnvData), replace = TRUE)
    diffCnv <- diff_CNV(cnvData, sampleGroup)

Use `clusterProfiler` to do enrichment analytics:

    pvalues <- diffCnv$pvalue * sign(diffCnv$odds)
    genes <- rownames(diffCnv)[diffCnv$pvalue < 0.05]
    library(clusterProfiler)
    library(enrichplot)
    library(org.Hs.eg.db)
    ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
    dotplot(ego)

## Difference analysis of single nucleotide Variation data

Use TCGAbiolinks to download TCGA data

    library(TCGAbiolinks)
    query <- GDCquery(project = "TCGA-ACC",
                      data.category = "Simple Nucleotide Variation",
                      data.type = "Masked Somatic Mutation",
                      workflow.type = "MuSE Variant Aggregation and Masking")

    GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path)

    data_snp <- GDCprepare(query = query,
                       directory =  "Your_Path")

Use `diff_SNP_tcga` to do difference analysis

    samples <- unique(data_snp$Tumor_Sample_Barcode)
    sampleType <- sample(c("A","B"), length(samples), replace = TRUE)
    names(sampleType) <- samples
    pvalue <- diff_SNP_tcga(snpData = data_snp, sampleType = sampleType)

Use `clusterProfiler` to do enrichment analysis

    pvalue2 <- sort(pvalue, decreasing = TRUE)
    library(clusterProfiler)
    library(enrichplot)
    library(org.Hs.eg.db)
    gsego <- gseGO(pvalue2, OrgDb = org.Hs.eg.db, keyType = "SYMBOL")
    dotplot(gsego)

## GEO chip data processing

The function `gene_ave` could average the expression data of different ids for the same gene in the GEO chip data. For example:

    aa <- c("MARCH1","MARC1","MARCH1","MARCH1","MARCH1")
    bb <- c(2.969058399,4.722410064,8.165514853,8.24243893,8.60815086)
    cc <- c(3.969058399,5.722410064,7.165514853,6.24243893,7.60815086)
    file_gene_ave <- data.frame(aa=aa,bb=bb,cc=cc)
    colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983")
    result <- gene_ave(file_gene_ave, 1)

Multiple genes symbols may correspond to a same chip id. The result of function `rep1` is to assign the expression of this id to each gene, and function `rep2` deletes the expression. For example:

    aa <- c("MARCH1 /// MMA","MARC1","MARCH2 /// MARCH3",
            "MARCH3 /// MARCH4","MARCH1")
    bb <- c("2.969058399","4.722410064","8.165514853","8.24243893","8.60815086")
    cc <- c("3.969058399","5.722410064","7.165514853","6.24243893","7.60815086")
    input_file <- data.frame(aa=aa,bb=bb,cc=cc)
    rep1_result <- rep1(input_file," /// ")
    rep2_result <- rep2(input_file," /// ")

## Other downstream analyses

1. The function `id_conversion_vector` could convert gene id from one of `symbol`, `RefSeq_ID`, `Ensembl_ID`, `NCBI_Gene_ID`, `UCSC_ID`, and `UniProt_ID` , etc. to another. Use `id_ava()` to get all the convertible ids. For example:

    id_conversion_vector("symbol", "ensembl_gene_id", c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"))
    #> 80% were successfully converted.
    #>        from              to
    #> 1     A2ML1 ENSG00000166535
    #> 2 A2ML1-AS1 ENSG00000256661
    #> 3    A4GALT ENSG00000128274
    #> 4      AAAS ENSG00000094914

When the user converts the Ensembl ID to other ids, the version number needs to be removed. For example, “ENSG00000186092.4” doesn’t work, you need to change it to “ENSG00000186092”.

Especially, the function `id_conversion` could convert ENSEMBL gene id to gene Symbol in TCGA. For example:

    profile <- GeoTcgaData::profile
    result <- id_conversion(profile)

The parameter `profile` is a data.frame or matrix of gene expression data in TCGA.

Note: In previous versions(< 1.0.0) the `id_conversion` and `id_conversion_vector` used HGNC data to convert human gene id. In future versions, we will use `clusterProfiler::bitr` for ID conversion.

    library(clusterProfiler)
    #>
    #> clusterProfiler v4.1.4.991  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
    #>
    #> If you use clusterProfiler in published research, please cite:
    #> T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
    #>
    #> 载入程辑包：'clusterProfiler'
    #> The following object is masked from 'package:stats':
    #>
    #>     filter
    library(org.Hs.eg.db)
    #> 载入需要的程辑包：AnnotationDbi
    #> 载入需要的程辑包：stats4
    #> 载入需要的程辑包：BiocGenerics
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    #> The following objects are masked from 'package:stats':
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    #>     as.data.frame, basename, cbind, colnames, dirname, do.call,
    #>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    #>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    #>     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    #>     tapply, union, unique, unsplit, which.max, which.min
    #> 载入需要的程辑包：Biobase
    #> Welcome to Bioconductor
    #>
    #>     Vignettes contain introductory material; view with
    #>     'browseVignettes()'. To cite Bioconductor, see
    #>     'citation("Biobase")', and for packages 'citation("pkgname")'.
    #> 载入需要的程辑包：IRanges
    #> 载入需要的程辑包：S4Vectors
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    #> The following object is masked from 'package:clusterProfiler':
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    #> 载入程辑包：'AnnotationDbi'
    #> The following object is masked from 'package:clusterProfiler':
    #>
    #>     select
    #>
    bitr(c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"), fromType = "SYMBOL",
        toType = "ENSEMBL", OrgDb = org.Hs.eg.db, drop = FALSE)
    #> 'select()' returned 1:1 mapping between keys and columns
    #> Warning in bitr(c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"), fromType =
    #> "SYMBOL", : 40% of input gene IDs are fail to map...
    #>      SYMBOL         ENSEMBL
    #> 1     A2ML1 ENSG00000166535
    #> 2 A2ML1-AS1            <NA>
    #> 3    A4GALT ENSG00000128274
    #> 4     A12M1            <NA>
    #> 5      AAAS ENSG00000094914

2. The function `countToFpkm_matrix` and `countToTpm_matrix` could convert count data to FPKM or TPM data.

    lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9),ncol=3)
    rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
    colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
    jieguo <- countToFpkm_matrix(lung_squ_count2)

    lung_squ_count2 <- matrix(c(0.11,0.22,0.43,0.14,0.875,0.66,0.77,0.18,0.29),ncol=3)
    rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
    colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
    jieguo <- countToTpm_matrix(lung_squ_count2)

3. The function `tcga_cli_deal` could combine clinical information obtained from TCGA and extract survival data. For example:

    tcga_cli <- tcga_cli_deal(system.file(file.path("extdata","tcga_cli"),package="GeoTcgaData"))

Note: Now the combined clinical data can be downloaded directly from TCGAbiolinks.

    library(TCGAbiolinks)
    ## get BCR Biotab data
    query <- GDCquery(project = "TCGA-ACC",
                      data.category = "Clinical",
                      data.type = "Clinical Supplement",
                      data.format = "BCR Biotab")
    GDCdownload(query)
    clinical.BCRtab.all <- GDCprepare(query)
    names(clinical.BCRtab.all)

    ## get indexed data
    clinical <- GDCquery_clinic(project = "TCGA-ACC", type = "clinical")