├── .Rbuildignore ├── .gitignore ├── DESCRIPTION ├── NAMESPACE ├── NEWS.md ├── R ├── CNV.R ├── Merge_methylation.R ├── RNA_seq.R ├── SNP.R ├── TCGA_id_conversion.R ├── arrayDiff.R ├── calculate_mean_module.R ├── calculate_mean_profile.R ├── data.R ├── fpkm_count_conversion.r ├── metap.R ├── prepareChi.r ├── rep.R └── sysdata.rda ├── README.Rmd ├── README.md ├── data ├── GSE66705_sample2.rda ├── geneExpress.rda ├── gene_cov.rda ├── kegg_liver.rda ├── module.rda ├── profile.rda └── ventricle.rda ├── docs ├── news │ └── index.html ├── reference │ ├── arrayDiff.html │ └── index.html └── sitemap.xml ├── inst └── extdata │ ├── build_data.R │ ├── cnv │ ├── 00de3 │ │ └── HONGS_p_TCGAb3_75_76_77_NSP_G.txt │ ├── 00e8 │ │ └── MICHE_p_TCGAb_428_429_NS.txt │ ├── 00f9 │ │ └── MINAE_p_TCGA_200_202_203_S.txt │ └── 0a01 │ │ └── KNELT_p_TCGA_b123_131_S.txt │ ├── methy │ ├── 0a0b4 │ │ ├── jhu-usc.e.H.4.lvl-3.TCGA-13-1405-01A-01D-0460-05.g.txt │ │ └── logs │ │ │ └── file1.parcel │ ├── 0a6b │ │ ├── jhu-usc.e.H.10.lvl-3.TCGA-30-1880-01A-01D-0652-05.g.txt │ │ └── logs │ │ │ └── file2.parcel │ ├── 0ae7 │ │ ├── jhu-usc.H.8.l.I.TCGA-30-1714-01A-02D-0563-05.g.txt │ │ └── logs │ │ │ └── file3.parcel │ └── 0b32 │ │ ├── jhu-usc.e.H.5.l.TCGA-13-1510-01A-02D-0475-05.gdc_hg38.txt │ │ └── logs │ │ └── file4.parcel │ └── tcga_cli │ ├── at.orl.TCGA-2V-A95S.xml │ ├── ati.org.TCGA-2Y-A9GT.xml │ └── ats.org.TCGA-2Y-A9GS.xml ├── man ├── GSE66705_sample2.Rd ├── Merge_methy_tcga.Rd ├── SNP_QC.Rd ├── array_preprocess.Rd ├── cal_mean_module.Rd ├── cluster_array.Rd ├── combine_pvalue.Rd ├── countToFpkm.Rd ├── countToTpm.Rd ├── differential_RNA.Rd ├── differential_SNP.Rd ├── differential_SNP_GEO.Rd ├── differential_SNP_tcga.Rd ├── differential_array.Rd ├── differential_cnv.Rd ├── differential_limma.Rd ├── differential_methy.Rd ├── fpkmToTpm.Rd ├── geneExpress.Rd ├── gene_ave.Rd ├── gene_cov.Rd ├── get_geo_array.Rd ├── id_conversion_TCGA.Rd ├── kegg_liver.Rd ├── module.Rd ├── prepare_chi.Rd ├── profile.Rd ├── repAssign.Rd ├── repRemove.Rd └── ventricle.Rd ├── tests ├── testthat.R └── testthat │ ├── fpkmToTpm.R │ ├── test_Merge_methy_tcga.R │ ├── test_arrayDiff.R │ ├── test_cal_mean_module.R │ ├── test_countToFpkm.R │ ├── test_countToTpm.R │ ├── test_diff_RNA_ucsc.R │ ├── test_differential_CNV.R │ ├── test_differential_RNA.R │ ├── test_differential_limma.R │ ├── test_gene_ave.R │ ├── test_prepare_chi.R │ ├── test_repAssign.R │ └── test_repRemove.R └── vignettes ├── .gitignore └── GeoTcgaData.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | ^data-raw$ 4 | ^README\.Rmd$ 5 | ^cran-comments\.md$ 6 | ^NEWS\.md$ 7 | ^.*\.jpg$ 8 | ^\.github$ 9 | ^\.json$ 10 | ^Thumbs\.db$ 11 | ^build_site.R$ 12 | ^pkgdown$ 13 | ^\.DS_Store 14 | ^_drake\.R$ 15 | ^\.drake$ 16 | ^\.drake_history$ 17 | ^\.future$ 18 | ^\.git$ 19 | ^\.github$ 20 | ^\.gitignore$ 21 | ^\.Rapp.history$ 22 | ^\.RData$ 23 | ^\.Rbuildignore$ 24 | ^\.Rhistory$ 25 | ^\.lintr$ 26 | ^CODE_OF_CONDUCT.md$ 27 | ^CONTRIBUTING.md$ 28 | ^pkgdown\.R$ 29 | ^pkgdown\.sh$ 30 | ^paper\.bib$ 31 | ^paper\.md$ 32 | ^LICENSE$ 33 | ^NOTICE$ 34 | vignettes/.*\.html 35 | vignettes/.*\.md 36 | vignettes/.*\.log 37 | vignettes/.*_files 38 | ^.*\.css$ 39 | ^.*\.gcda$ 40 | ^.*\.gcno$ 41 | ^.*\.js$ 42 | ^.*\.log$ 43 | ^.*\.out$ 44 | ^.*\.nfs.*$ 45 | ^.*\.svg$ 46 | ^.*\.yaml$ 47 | ^.*\.yml$ 48 | ^docs$ 49 | ^_pkgdown\.yml$ 50 | ^codemeta\.json$ 51 | ^\.httr-oauth$ 52 | ^CRAN-RELEASE$ 53 | CONDUCT.md 54 | 55 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | inst/doc 6 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: GeoTcgaData 2 | Type: Package 3 | Title: Processing Various Types of Data on GEO and TCGA 4 | Version: 1.99.2 5 | Authors@R: person(given = "Erqiang", family = "Hu", email = "13766876214@163.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1798-7513")) 6 | Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) 7 | provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP 8 | and Copy number variation data. It's easy to download data from TCGA using the 9 | gdc tool, but processing these data into a format suitable for bioinformatics 10 | analysis requires more work. This R package was developed to handle these data. 11 | Depends: R (>= 4.2.0) 12 | License: Artistic-2.0 13 | Encoding: UTF-8 14 | RoxygenNote: 7.2.3 15 | Suggests: 16 | knitr, 17 | rmarkdown, 18 | DESeq2, 19 | S4Vectors, 20 | ChAMP, 21 | impute, 22 | tidyr, 23 | clusterProfiler, 24 | org.Hs.eg.db, 25 | edgeR, 26 | limma, 27 | quantreg, 28 | minfi, 29 | IlluminaHumanMethylation450kanno.ilmn12.hg19, 30 | dearseq, 31 | NOISeq, 32 | testthat (>= 3.0.0), 33 | CATT, 34 | TCGAbiolinks, 35 | enrichplot, 36 | GEOquery, 37 | BiocGenerics 38 | VignetteBuilder: knitr 39 | Imports: 40 | utils, 41 | data.table, 42 | plyr, 43 | cqn, 44 | topconfects, 45 | stats, 46 | SummarizedExperiment, 47 | methods 48 | Language: en-US 49 | URL: https://github.com/YuLab-SMU/GeoTcgaData 50 | BugReports: https://github.com/YuLab-SMU/GeoTcgaData/issues 51 | biocViews: GeneExpression, DifferentialExpression, RNASeq, 52 | CopyNumberVariation, Microarray, Software, DNAMethylation, 53 | DifferentialMethylation, SNP, ATACSeq, MethylationArray 54 | Config/testthat/edition: 3 55 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(Merge_methy_tcga) 4 | export(SNP_QC) 5 | export(array_preprocess) 6 | export(cal_mean_module) 7 | export(cluster_array) 8 | export(combine_pvalue) 9 | export(countToFpkm) 10 | export(countToTpm) 11 | export(differential_CNV) 12 | export(differential_RNA) 13 | export(differential_SNP) 14 | export(differential_SNP_GEO) 15 | export(differential_SNP_tcga) 16 | export(differential_array) 17 | export(differential_limma) 18 | export(differential_methy) 19 | export(fpkmToTpm) 20 | export(gene_ave) 21 | export(get_geo_array) 22 | export(id_conversion_TCGA) 23 | export(prepare_chi) 24 | export(repAssign) 25 | export(repRemove) 26 | import(cqn) 27 | import(methods) 28 | importFrom(SummarizedExperiment,assays) 29 | importFrom(SummarizedExperiment,colData) 30 | importFrom(plyr,rename) 31 | importFrom(stats,as.dist) 32 | importFrom(stats,cor) 33 | importFrom(stats,cutree) 34 | importFrom(stats,hclust) 35 | importFrom(stats,na.fail) 36 | importFrom(stats,p.adjust) 37 | importFrom(stats,pchisq) 38 | importFrom(stats,pnorm) 39 | importFrom(stats,qnorm) 40 | importFrom(utils,methods) 41 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # GeoTcgaData 0.99.2 2 | 3 | + use SummarizedExperiment input (2023-1-29, Sun) 4 | + fix return value of `differential_array` (2022-10-8, Sat) 5 | + fix gene length bug in `countToTpm()` and `countToFpkm()`(2022_9_22, Tue) 6 | + fix a bug in `id_conversion` (2022-8-27, Sat) 7 | + fix a bug in `differential_RNA(useTopconfects = TRUE)` (2022-8-12, Fir) 8 | + add function `methydifferential_ucsc` and `methydifferential_limma`(2021-10-24, Sun) 9 | + update hgnc_file data(2021-10-24, Sun) 10 | + add function `differential_RNA` to do difference analysis of RNA-seq data(2021-7-20, Tue) 11 | + add data hgnc_file 12 | + update function: id_ava() 13 | + add functions: ann_merge(), countToFpkm(), countToTpm() 14 | 15 | -------------------------------------------------------------------------------- /R/CNV.R: -------------------------------------------------------------------------------- 1 | #' Do difference analysis of gene level copy number variation data 2 | #' 3 | #' @param cnvData data.frame of CNV data, each column is a sample, 4 | #' and each row is a CNV. 5 | #' @param sampleGroup vector of sample group 6 | #' @param method method to do diffenenital analysis, 7 | #' one of "Chisquare", "fisher", 8 | #' and "CATT"(Cochran-Armitage trend test) 9 | #' @param adjust.method adjust.method, one of "holm", "hochberg", "hommel", 10 | #' "bonferroni", "BH", "BY", "fdr", and "none". 11 | #' @param ... parameters for "Chisquare", "fisher", 12 | #' and "CATT"(Cochran-Armitage trend test) 13 | #' @return data.frame with pvalue and estimate 14 | #' @export 15 | #' 16 | #' @examples 17 | #' \donttest{ 18 | #' # use TCGAbiolinks data as example 19 | #' library(TCGAbiolinks) 20 | #' query <- GDCquery( 21 | #' project = "TCGA-ACC", 22 | #' data.category = "Copy Number Variation", 23 | #' data.type = "Gene Level Copy Number", 24 | #' access = "open" 25 | #' ) 26 | #' GDCdownload(query) 27 | #' cnvData <- GDCprepare(query) 28 | #' aa <- assays(cnvData)$copy_number 29 | #' bb <- aa 30 | #' aa[bb == 2] <- 0 31 | #' aa[bb < 2] <- -1 32 | #' aa[bb > 2] <- 1 33 | #' sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE) 34 | #' diffCnv <- differential_CNV(aa, sampleGroup) 35 | #' 36 | #' # Use sangerbox CNV data as example 37 | #' cnvData <- fread("Merge_GeneLevelCopyNumber.txt") 38 | #' class(cnvData) <- "data.frame" 39 | #' rownames(cnvData) <- cnvData[, 1] 40 | #' cnvData <- cnvData[, -c(1, 2, 3)] 41 | #' sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE) 42 | #' diffCnv <- differential_CNV(cnvData, sampleGroup) 43 | #' } 44 | #' # use random data as example 45 | #' aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) 46 | #' rownames(aa) <- paste0("gene", 1:25) 47 | #' colnames(aa) <- paste0("sample", 1:8) 48 | #' sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) 49 | #' diffCnv <- differential_CNV(aa, sampleGroup) 50 | differential_CNV <- function(cnvData, sampleGroup, 51 | method = "Chisquare", 52 | adjust.method = "BH", ...) { 53 | type1 <- which(sampleGroup == unique(sampleGroup)[1]) 54 | type2 <- which(sampleGroup == unique(sampleGroup)[2]) 55 | pvalue <- rep(1, nrow(cnvData)) 56 | estimate <- rep(0, nrow(cnvData)) 57 | for (i in seq_len(nrow(cnvData))) { 58 | type1_freq <- table(as.character(cnvData[i, type1])) 59 | type2_freq <- table(as.character(cnvData[i, type2])) 60 | df <- data.frame( 61 | type1 = as.numeric(type1_freq[c("-1", "0", "1")]), 62 | type2 = as.numeric(type2_freq[c("-1", "0", "1")]) 63 | ) 64 | df[is.na(df)] <- 0 65 | df <- df[rowSums(df) > 0, ] 66 | if (nrow(df) > 2) { 67 | if (method == "fisher") { 68 | fish <- stats::fisher.test(df, ...) 69 | pvalue[i] <- fish$p.value 70 | if (nrow(df) == 2) { 71 | estimate[i] <- fish$estimate 72 | } 73 | } 74 | 75 | if (method == "Chisquare") { 76 | pvalue[i] <- stats::chisq.test(df, ...)$p.value 77 | } 78 | 79 | if(method == "CATT") { 80 | pvalue[i] <- CATT::CATT(table = t(df), ...)$p.value 81 | } 82 | } 83 | } 84 | 85 | adj.P.Val <- stats::p.adjust(pvalue, method = adjust.method) 86 | gene <- gsub("\\..*", "", rownames(cnvData)) 87 | result <- data.frame(gene = gene, P.Value = pvalue, 88 | adj.P.Val = adj.P.Val, estimate = estimate) 89 | rownames(result) <- gene 90 | result 91 | } 92 | 93 | -------------------------------------------------------------------------------- /R/Merge_methylation.R: -------------------------------------------------------------------------------- 1 | #' Get methylation difference gene 2 | #' 3 | #' @title differential_methy 4 | #' @rdname differential_methy 5 | #' @param cpgData data.frame of cpg beta value, , or SummarizedExperiment object 6 | #' @param sampleGroup vector of sample group 7 | #' @param groupCol group column 8 | #' @param combineMethod method to combine the cpg pvalues, 9 | #' a function or one of "stouffer", "fisher" and "rhoScores". 10 | #' @param missing_value Method to impute missing expression data, 11 | #' one of "zero" and "knn". 12 | #' @param cpg2gene data.frame to annotate cpg locus to gene 13 | #' @param normMethod Method to do normalization: "PBC" or "BMIQ". 14 | #' @param region region of genes, one of "Body", "TSS1500", "TSS200", 15 | #' "3'UTR", "1stExon", "5'UTR", and "IGR". Only used when cpg2gene is NULL. 16 | #' @param model if "cpg", step1: calculate difference cpgs; 17 | #' step2: calculate difference genes. 18 | #' if "gene", step1: calculate the methylation level of genes; 19 | #' step2: calculate difference genes. 20 | #' @param adjust.method character string specifying the method 21 | #' used to adjust p-values for multiple testing. 22 | #' See \link{p.adjust} for possible values. 23 | #' @param ucscData Logical, whether the data comes from UCSC Xena. 24 | #' @param adjPvalCutoff adjusted pvalue cutoff 25 | #' @importFrom stats p.adjust 26 | #' @return data.frame 27 | #' @export 28 | #' @examples 29 | #' \donttest{ 30 | #' # use TCGAbiolinks data 31 | #' library(TCGAbiolinks) 32 | #' query <- GDCquery(project = "TCGA-ACC", 33 | #' data.category = "DNA Methylation", 34 | #' data.type = "Methylation Beta Value", 35 | #' platform = "Illumina Human Methylation 450") 36 | #' GDCdownload(query, method = "api", files.per.chunk = 5, 37 | #' directory = Your_Path) 38 | #' merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data) 39 | #' library(ChAMP) # To avoid reporting errors 40 | #' differential_gene <- differential_methy(cpgData = merge_result, 41 | #' sampleGroup = sample(c("C","T"), 42 | #' ncol(merge_result[[1]]), replace = TRUE)) 43 | #' } 44 | #' # use user defined data 45 | #' library(ChAMP) 46 | #' cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 47 | #' rownames(cpgData) <- paste0("cpg", seq_len(200)) 48 | #' colnames(cpgData) <- paste0("sample", seq_len(10)) 49 | #' sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 50 | #' names(sampleGroup) <- colnames(cpgData) 51 | #' cpg2gene <- data.frame(cpg = rownames(cpgData), 52 | #' gene = rep(paste0("gene", seq_len(20)), 10)) 53 | #' result <- differential_methy(cpgData, sampleGroup, 54 | #' cpg2gene = cpg2gene, normMethod = NULL) 55 | #' # use SummarizedExperiment object input 56 | #' library(ChAMP) 57 | #' cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 58 | #' rownames(cpgData) <- paste0("cpg", seq_len(200)) 59 | #' colnames(cpgData) <- paste0("sample", seq_len(10)) 60 | #' sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 61 | #' names(sampleGroup) <- colnames(cpgData) 62 | #' cpg2gene <- data.frame(cpg = rownames(cpgData), 63 | #' gene = rep(paste0("gene", seq_len(20)), 10)) 64 | #' colData <- S4Vectors::DataFrame( 65 | #' row.names = colnames(cpgData), 66 | #' group = sampleGroup 67 | #' ) 68 | #' data <- SummarizedExperiment::SummarizedExperiment( 69 | #' assays=S4Vectors::SimpleList(counts=cpgData), 70 | #' colData = colData) 71 | #' result <- differential_methy(cpgData = data, 72 | #' groupCol = "group", normMethod = NULL, 73 | #' cpg2gene = cpg2gene) 74 | differential_methy <- function(cpgData, sampleGroup, 75 | groupCol, 76 | # combineMethod = RobustRankAggreg::rhoScores, 77 | combineMethod = "stouffer", 78 | missing_value = "knn", 79 | cpg2gene = NULL, 80 | normMethod = "PBC", 81 | region = "TSS1500", 82 | model = "gene", 83 | adjust.method = "BH", 84 | adjPvalCutoff = 0.05, 85 | ucscData = FALSE) { 86 | region <- match.arg(region, c("Body", "TSS1500", "TSS200", 87 | "3'UTR", "1stExon", "5'UTR", "IGR")) 88 | model <- match.arg(model, c("cpg", "gene")) 89 | 90 | if (inherits(cpgData, "SummarizedExperiment")) { 91 | cpgData2 <- cpgData 92 | cpgData <- assays(cpgData2)$counts 93 | sampleGroup <- colData(cpgData2)[, groupCol] 94 | names(sampleGroup) <- rownames(colData(cpgData2)) 95 | } else { 96 | if (inherits(cpgData, "SummarizedExperiment")) { 97 | cpgData <- cpgData[[1]] 98 | } 99 | } 100 | 101 | 102 | if (ucscData) { 103 | class(methy) <- "data.frame" 104 | rownames(methy) <- methy[, 1] 105 | cpgs <- rownames(methy) 106 | methy <- methy[, -1] 107 | group <- sampleGroup 108 | if (is.null(group)) { 109 | group <- lapply(colnames(methy), function(x) { 110 | strsplit(x, "-")[[1]][4] 111 | }) |> unlist() 112 | 113 | group <- substring(group, 1, 1) 114 | } 115 | } 116 | 117 | 118 | 119 | cpgData <- as.matrix(cpgData) 120 | # Use KNN to fill in missing values 121 | if (missing_value == "zero") { 122 | cpgData[is.na(cpgData)] <- 0 123 | data.m <- cpgData 124 | } else { 125 | data.m <- impute::impute.knn(cpgData)$data 126 | } 127 | 128 | # normalize data 129 | myNorm <- data.m 130 | if (!is.null(normMethod)) { 131 | myNorm <- ChAMP::champ.norm(beta = data.m, rgSet = NULL, 132 | mset = NULL, method = normMethod) 133 | } 134 | if (!is.null(cpg2gene)) { 135 | cpg_gene <- cpg2gene 136 | } else { 137 | cpg_gene <- get_cpg_annotation(region = region) 138 | } 139 | 140 | 141 | if (model == "gene") { 142 | cpg_gene <- split(cpg_gene[, 2], cpg_gene[, 1]) 143 | genes <- unlist(lapply(cpg_gene, function(x) {paste(x,collapse = ";")})) 144 | cpg_gene <- data.frame(cpg = names(cpg_gene), gene = genes) 145 | rownames(cpg_gene) <- cpg_gene[, 1] 146 | myNorm <- as.data.frame(myNorm) 147 | myNorm$gene <- cpg_gene[rownames(myNorm), 2] 148 | # myNorm <- myNorm[, c(ncol(myNorm), 1:(ncol(myNorm) - 1))] 149 | myNorm <- myNorm[, c(ncol(myNorm), seq_len(ncol(myNorm) - 1))] 150 | myNorm <- myNorm[!is.na(myNorm$gene), ] 151 | 152 | 153 | myNorm$gene <- as.character(myNorm$gene) 154 | myNorm2 <- repAssign(myNorm, ";") 155 | myNorm3 <- gene_ave(myNorm2) 156 | 157 | ## use limma to do differential expression analysis 158 | gene_pvalue <- differential_limma(myNorm3, group = sampleGroup, 159 | adjust.method = adjust.method) 160 | gene_pvalue$gene <- rownames(gene_pvalue) 161 | } else { 162 | # Identify Differential Methylation Positions (DMP) 163 | myDMP <- ChAMP::champ.DMP(beta = myNorm, 164 | pheno = sampleGroup, adjPVal = 1) 165 | myDMP <- as.data.frame(myDMP) 166 | 167 | # use cpg_gene to annotate CpGs 168 | pvalues <- cpg_gene 169 | pvalues$pvalue <- myDMP[cpg_gene[, 1], 4] 170 | # rownames(pvalues) <- pvalues[, 1] 171 | pvalues <- pvalues[!is.na(pvalues$pvalue), ] 172 | 173 | if (is.function(combineMethod)) { 174 | gene_pvalue <- stats::aggregate(pvalues[, 4], 175 | by = list(pvalues[, 2]), 176 | # FUN = combine_pvalue, combineMethod = combineMethod 177 | FUN = combineMethod 178 | ) 179 | colnames(gene_pvalue) <- c("gene", "pvalue") 180 | } else { 181 | aa <- pvalues$pvalue 182 | bb <- split(aa, pvalues$gene) 183 | gene_pvalue <- data.frame(gene = names(bb), 184 | pvalue = unlist(lapply(bb, function(x) x[1]))) 185 | if (combineMethod == "stouffer") { 186 | 187 | myBetas <- myNorm[pvalues$cpg, ] 188 | myBetas <- split(as.data.frame(myBetas), pvalues$gene) 189 | correl <- lapply(myBetas, function(x) cor(t(x))) 190 | weights <- lapply(correl, function(x) 1/apply(x^2,1,sum)) 191 | 192 | for (i in seq_len(nrow(gene_pvalue))) { 193 | if (length(bb[[i]]) > 1) { 194 | gene_pvalue[i, 2] <- sumz(bb[[i]], weights[[i]])$p 195 | } 196 | } 197 | } 198 | 199 | if (combineMethod == "fisher") { 200 | for (i in seq_len(nrow(gene_pvalue))) { 201 | if (length(bb[[i]]) > 1) { 202 | gene_pvalue[i, 2] <- sumlog(bb[[i]])$p 203 | } 204 | } 205 | } 206 | } 207 | 208 | 209 | 210 | # get logFC of genes 211 | myNorm2 <- myNorm[pvalues[, 1], ] 212 | myNorm2 <- stats::aggregate(myNorm2, 213 | by = list(pvalues[, 2]), FUN = mean) 214 | 215 | myNorm2 <- myNorm2[myNorm2[, 1] != "", ] 216 | rownames(myNorm2) <- myNorm2[, 1] 217 | myNorm2 <- myNorm2[, -1] 218 | groups <- sort(unique(sampleGroup)) 219 | mean1 <- rowMeans(myNorm2[, sampleGroup == groups[1]], na.rm = TRUE) 220 | mean2 <- rowMeans(myNorm2[, sampleGroup == groups[2]], na.rm = TRUE) 221 | logFC <- mean1 - mean2 222 | 223 | gene_pvalue$logFC <- logFC[gene_pvalue[, 1]] 224 | colnames(gene_pvalue) <- c("gene", "P.Value", "logFC") 225 | gene_pvalue$gene <- as.character(gene_pvalue$gene) 226 | gene_pvalue$adj.P.Val <- p.adjust(gene_pvalue$P.Value, 227 | method = adjust.method) 228 | rownames(gene_pvalue) <- gene_pvalue$gene 229 | } 230 | gene_pvalue <- gene_pvalue[gene_pvalue$adj.P.Val < adjPvalCutoff, ] 231 | return(gene_pvalue) 232 | } 233 | 234 | 235 | 236 | #' differential_limma 237 | #' 238 | #' @param df data.frame of the omic data 239 | #' @param group a vector, group of samples. 240 | #' @param adjust.method adjust.method. 241 | #' @return data.frame 242 | #' @export 243 | #' @examples 244 | #' df <- matrix(runif(200), 25, 8) 245 | #' df <- as.data.frame(df) 246 | #' rownames(df) <- paste0("gene", 1:25) 247 | #' colnames(df) <- paste0("sample", 1:8) 248 | #' group <- sample(c("group1", "group2"), 8, replace = TRUE) 249 | #' result <- differential_limma(df = df, group = group) 250 | differential_limma <- function(df, group, adjust.method = "BH") { 251 | groups <- unique(group) 252 | # if group is a numberic vector(even for c("0", "1")), will get errors. 253 | group <- gsub(groups[1], "nromal", group) 254 | group <- gsub(groups[2], "disease", group) 255 | design <- stats::model.matrix(~ 0 + factor(group)) 256 | colnames(design) <- levels(factor(group)) 257 | contrast.matrix <- limma::makeContrasts( 258 | contrasts = paste(colnames(design)[2:1], 259 | collapse = "-" 260 | ), levels = colnames(design)) 261 | 262 | fit <- limma::lmFit(df, design) 263 | fit <- limma::contrasts.fit(fit, contrast.matrix) 264 | fit <- limma::eBayes(fit) 265 | limma::topTable(fit, adjust.method = adjust.method, number = Inf) 266 | ## or limma::topTable(fit, coef = 1, adjust='BH', number=Inf) 267 | ## contrasts.fit is not necessory 268 | # groups <- unique(group) 269 | # group <- gsub(groups[1], "nromal", group) 270 | # group <- gsub(groups[2], "disease", group) 271 | # design <- stats::model.matrix(~factor(group)) 272 | 273 | # fit2 <- lmFit(df, design) 274 | # fit2 <- eBayes(fit2) 275 | # topTable(fit2,coef=2, adjust='BH', number=Inf) 276 | 277 | ## coef parameter is not necessory： 278 | # opTable(fit2, adjust='BH', number=Inf) 279 | } 280 | 281 | #' Merge methylation data downloaded from TCGA 282 | #' 283 | #' When the methylation data is downloaded from TCGA, 284 | #' each sample is saved in a folder, which contains the methylation value file 285 | #' and the descriptive file. This function can directly 286 | #' extract and consolidate all folders. 287 | #' @param dirr a string for the directory of methylation data download from tcga 288 | #' useing the tools gdc 289 | #' @return a matrix, a combined methylation expression spectrum matrix 290 | #' @export 291 | #' 292 | #' @examples 293 | #' merge_result <- Merge_methy_tcga(system.file(file.path("extdata", "methy"), 294 | #' package = "GeoTcgaData")) 295 | Merge_methy_tcga <- function(dirr = NULL) { 296 | options(warn = -1) 297 | # file_num=1 298 | if (is.null(dirr)) stop("please give your directory of methylation data!") 299 | tcga_dir <- dir(dirr) 300 | filePath <- file.path(dirr, tcga_dir[1]) 301 | methyFile <- get_methy_df(filePath) 302 | methyResult <- matrix(0, nrow = nrow(methyFile), ncol = length(tcga_dir)) 303 | rownames(methyResult) <- methyFile[, "Composite Element REF"] 304 | samples <- rep(0, length(tcga_dir)) 305 | methyResult[, 1] <- methyFile[, 2] 306 | samples[1] <- colnames(methyFile)[2] 307 | message("file", 1, " is over") 308 | for (i in 2:length(tcga_dir)) { 309 | message("file", i, " is over") 310 | filePath <- file.path(dirr, tcga_dir[i]) 311 | methyFile <- get_methy_df(filePath) 312 | methyResult[, i] <- methyFile[, 2] 313 | samples[i] <- colnames(methyFile)[2] 314 | gc() 315 | } 316 | colnames(methyResult) <- samples 317 | cpg_info <- methyFile[, -2] 318 | return(list(methyResult = methyResult, cpg_info = cpg_info)) 319 | } 320 | 321 | #' Read methylated data file and turn it into data frame 322 | #' 323 | #' @param filePath Path of files 324 | #' @return data.frame 325 | #' @noRd 326 | get_methy_df <- function(filePath) { 327 | methyDir <- dir(filePath) 328 | for (j in seq_len(length(methyDir))) { 329 | if (length(grep("jhu-usc", methyDir[j])) > 0) { 330 | file_name <- file.path(filePath, dir(filePath)[j]) 331 | sample <- unlist(strsplit(dir(filePath)[j], "\\."))[6] 332 | } 333 | } 334 | methyFile <- data.table::fread(file_name, header = TRUE) 335 | class(methyFile) <- "data.frame" 336 | colnames(methyFile)[2] <- sample 337 | return(methyFile) 338 | } 339 | 340 | 341 | get_cpg_annotation <- function(region = "TSS1500") { 342 | ## library to avoid errors. 343 | # library(IlluminaHumanMethylation450kanno.ilmn12.hg19) 344 | ann <- minfi::getAnnotation( 345 | IlluminaHumanMethylation450kanno.ilmn12.hg19::IlluminaHumanMethylation450kanno.ilmn12.hg19) 346 | ann <- as.data.frame(ann) 347 | cpg_gene <- ann[, c("Name", "UCSC_RefGene_Name", "UCSC_RefGene_Group")] 348 | # cpg_gene <- cpg_gene[grep(region, cpg_gene$UCSC_RefGene_Group), ] 349 | cpg_gene <- cpg_gene[cpg_gene[, 2] != "", ] 350 | genelist <- strsplit(cpg_gene[, 2], ";") 351 | regionlist <- strsplit(cpg_gene[, 3], ";") 352 | geneLength <- unlist(lapply(genelist, length)) 353 | cpgs <- rep(cpg_gene[, 1], times = geneLength) 354 | cpg_gene2 <- data.frame(cpg = cpgs, gene = unlist(genelist), 355 | region = unlist(regionlist)) 356 | cpg_gene2 <- cpg_gene2[grep(region, cpg_gene2$region), ] 357 | return(unique(cpg_gene2)) 358 | } 359 | -------------------------------------------------------------------------------- /R/RNA_seq.R: -------------------------------------------------------------------------------- 1 | #' Do difference analysis of RNA-seq data 2 | #' 3 | #' @title differential_RNA 4 | #' @rdname differential_RNA 5 | #' @param counts a dataframe or numeric matrix of raw counts data, 6 | #' or SummarizedExperiment object 7 | #' @param group sample groups 8 | #' @param groupCol group column 9 | #' @param method one of "DESeq2", "edgeR" , "limma", "dearseq", 10 | #' "NOISeq", "Wilcoxon", and "auto". 11 | #' @param geneLength a vector of gene length. 12 | #' @param gccontent a vector of gene GC content. 13 | #' @param filter if TRUE, use filterByExpr to filter genes. 14 | #' @param edgeRNorm if TRUE, use edgeR to do normalization for dearseq method. 15 | #' @param adjust.method character string specifying the method used to 16 | #' adjust p-values for multiple testing. 17 | #' See \link{p.adjust} for possible values. 18 | #' @param useTopconfects if TRUE, use topconfects to provide a 19 | #' more biologically useful ranked gene list. 20 | #' @param ucscData Logical, whether the data comes from UCSC Xena. 21 | #' @importFrom plyr rename 22 | #' @importFrom SummarizedExperiment assays 23 | #' @importFrom SummarizedExperiment colData 24 | #' @importFrom utils methods 25 | #' @import methods 26 | #' @import cqn 27 | #' @return data.frame 28 | #' @export 29 | #' 30 | #' @examples 31 | #' \donttest{ 32 | # use `TCGAbiolinks` to download TCGA data 33 | #' library(TCGAbiolinks) 34 | #' 35 | #' query <- GDCquery( 36 | #' project = "TCGA-ACC", 37 | #' data.category = "Transcriptome Profiling", 38 | #' data.type = "Gene Expression Quantification", 39 | #' workflow.type = "STAR - Counts" 40 | #' ) 41 | #' 42 | #' GDCdownload(query, 43 | #' method = "api", files.per.chunk = 3, 44 | #' directory = Your_Path 45 | #' ) 46 | #' 47 | #' dataRNA <- GDCprepare( 48 | #' query = query, directory = Your_Path, 49 | #' save = TRUE, save.filename = "dataRNA.RData" 50 | #' ) 51 | #' ## get raw count matrix 52 | #' dataPrep <- TCGAanalyze_Preprocessing( 53 | #' object = dataRNA, 54 | #' cor.cut = 0.6, 55 | #' datatype = "STAR - Counts" 56 | #' ) 57 | #' 58 | #' # Use `differential_RNA` to do difference analysis. 59 | #' # We provide the data of human gene length and GC content in `gene_cov`. 60 | #' group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE) 61 | #' library(cqn) # To avoid reporting errors: there is no function "rq" 62 | #' ## get gene length and GC content 63 | #' library(org.Hs.eg.db) 64 | #' genes_bitr <- bitr(rownames(gene_cov), 65 | #' fromType = "ENTREZID", toType = "ENSEMBL", 66 | #' OrgDb = org.Hs.eg.db, drop = TRUE 67 | #' ) 68 | #' genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ] 69 | #' gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] 70 | #' rownames(gene_cov2) <- genes_bitr$ENSEMBL 71 | #' genes <- intersect(rownames(dataPrep), rownames(gene_cov2)) 72 | #' dataPrep <- dataPrep[genes, ] 73 | #' geneLength <- gene_cov2(genes, "length") 74 | #' gccontent <- gene_cov2(genes, "GC") 75 | #' names(geneLength) <- names(gccontent) <- genes 76 | #' ## Difference analysis 77 | #' DEGAll <- differential_RNA( 78 | #' counts = dataPrep, group = group, 79 | #' geneLength = geneLength, gccontent = gccontent 80 | #' ) 81 | #' # Use `clusterProfiler` to do enrichment analytics: 82 | #' diffGenes <- DEGAll$logFC 83 | #' names(diffGenes) <- rownames(DEGAll) 84 | #' diffGenes <- sort(diffGenes, decreasing = TRUE) 85 | #' library(clusterProfiler) 86 | #' library(enrichplot) 87 | #' library(org.Hs.eg.db) 88 | #' gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL") 89 | #' dotplot(gsego) 90 | #' } 91 | #' # use user-defined data 92 | #' df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 93 | #' df <- as.data.frame(df) 94 | #' rownames(df) <- paste0("gene", 1:25) 95 | #' colnames(df) <- paste0("sample", 1:16) 96 | #' group <- sample(c("group1", "group2"), 16, replace = TRUE) 97 | #' result <- differential_RNA(counts = df, group = group, 98 | #' filte = FALSE, method = "Wilcoxon") 99 | #' # use SummarizedExperiment object input 100 | #' df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 101 | #' rownames(df) <- paste0("gene", 1:25) 102 | #' colnames(df) <- paste0("sample", 1:16) 103 | #' group <- sample(c("group1", "group2"), 16, replace = TRUE) 104 | #' 105 | #' nrows <- 200; ncols <- 20 106 | #' counts <- matrix( 107 | #' runif(nrows * ncols, 1, 1e4), nrows, 108 | #' dimnames = list(paste0("cg",1:200),paste0("S",1:20)) 109 | #' ) 110 | #' 111 | #' colData <- S4Vectors::DataFrame( 112 | #' row.names = paste0("sample", 1:16), 113 | #' group = group 114 | #' ) 115 | #' data <- SummarizedExperiment::SummarizedExperiment( 116 | #' assays=S4Vectors::SimpleList(counts=df), 117 | #' colData = colData) 118 | #' 119 | #' result <- differential_RNA(counts = data, groupCol = "group", 120 | #' filte = FALSE, method = "Wilcoxon") 121 | #' @importFrom plyr rename 122 | #' @import cqn 123 | differential_RNA <- function(counts, group, groupCol, method = "limma", 124 | geneLength = NULL, 125 | gccontent = NULL, filter = TRUE, edgeRNorm = TRUE, 126 | adjust.method = "BH", useTopconfects = TRUE, 127 | ucscData = FALSE) { 128 | method <- match.arg(method, c("DESeq2", "edgeR", "limma", 129 | "dearseq", "Wilcoxon", "NOISeq", "auto")) 130 | 131 | if (ucscData) { 132 | class(counts) <- "data.frame" 133 | counts[, 1] <- gsub("\\..*", "", counts[, 1]) 134 | rownames(counts) <- counts[, 1] 135 | counts <- counts[, -1] 136 | counts <- round(2^counts) - 1 137 | } 138 | 139 | 140 | if (inherits(counts, "SummarizedExperiment")) { 141 | se <- counts 142 | counts <- assays(se)$counts 143 | group <- colData(se)[, groupCol] 144 | names(group) <- rownames(colData(se)) 145 | } 146 | 147 | 148 | cols <- !duplicated(colnames(counts)) 149 | counts <- counts[, cols] 150 | group <- group[cols] 151 | if (min(table(group)) > 4 && method == "auto") { 152 | method <- "Wilcoxon" 153 | } else { 154 | method <- "limma" 155 | } 156 | 157 | ## use cqn to correct the bias 158 | correct<- TRUE 159 | uCovar <- NULL 160 | if (is.null(geneLength) || is.null(gccontent)) { 161 | correct<- FALSE 162 | } else { 163 | genes_gc <- intersect(names(geneLength), names(gccontent)) 164 | uCovar <- data.frame(length = geneLength[genes_gc], 165 | gccontent = gccontent[genes_gc]) 166 | rownames(uCovar) <- genes_gc 167 | counts <- counts[genes_gc, ] 168 | } 169 | d.mont <- edgeR::DGEList(counts = counts, group = group, genes = uCovar) 170 | if (filter) { 171 | keep <- edgeR::filterByExpr(d.mont) 172 | d.mont <- d.mont[keep, keep.lib.sizes = FALSE] 173 | counts <- counts[keep, ] 174 | } 175 | if (correct) { 176 | geneLength <- geneLength[rownames(counts)] 177 | gccontent <- gccontent[rownames(counts)] 178 | cqn.subset <- cqn::cqn(counts, lengths = geneLength, x = gccontent) 179 | } 180 | 181 | 182 | if (method == "DESeq2") { 183 | coldata <- data.frame(group) 184 | 185 | dds <- DESeq2::DESeqDataSetFromMatrix( 186 | countData = counts, 187 | colData = coldata, design = ~group 188 | ) 189 | 190 | 191 | if (correct) { 192 | cqnOffset <- cqn.subset$glm.offset 193 | cqnNormFactors <- exp(cqnOffset) 194 | ## divide out the geometric mean 195 | ## https://support.bioconductor.org/p/89239/ 196 | ## https://support.bioconductor.org/p/95683/ 197 | normFactors <- cqnNormFactors / exp(rowMeans(log(cqnNormFactors))) 198 | DESeq2::normalizationFactors(dds) <- normFactors 199 | } 200 | DEGAll <- DESeq2::DESeq(dds) 201 | DEGAll_table <- NULL 202 | if (useTopconfects) { 203 | DEGAll_table <- 204 | topconfects::deseq2_confects(DEGAll, step = 0.05)$table 205 | rownames(DEGAll_table) <- DEGAll_table$name 206 | } 207 | DEGAll <- DEGAll |> 208 | DESeq2::results(pAdjustMethod = adjust.method) |> 209 | as.data.frame() |> 210 | rename(c("log2FoldChange" = "logFC")) |> 211 | rename(c("pvalue" = "P.Value")) |> 212 | rename(c("padj" = "adj.P.Val")) 213 | DEGAll$length <- geneLength[rownames(DEGAll)] 214 | DEGAll$gccontent <- gccontent[rownames(DEGAll)] 215 | # DEGAll <- DEGAll[, c(ncol(DEGAll) - 1, 216 | # ncol(DEGAll), 1:(ncol(DEGAll) - 2))] 217 | DEGAll <- DEGAll[, c(ncol(DEGAll) - 1, ncol(DEGAll), 218 | seq_len(ncol(DEGAll) - 2))] 219 | if (!is.null(DEGAll_table)) { 220 | genes <- intersect(rownames(DEGAll), rownames(DEGAll_table)) 221 | DEGAll <- cbind(DEGAll[genes, ], DEGAll_table[genes, ]) 222 | DEGAll <- DEGAll[order(DEGAll$P.Value), ] 223 | } 224 | } else { 225 | if (correct) { 226 | ## with cqn, there is no need to normalize 227 | ## using the normalization tools 228 | ## from edgeR, such as calcNormFactors. 229 | d.mont$offset <- cqn.subset$glm.offset 230 | } else { 231 | ## TMM Normalization 232 | d.mont <- edgeR::calcNormFactors(d.mont, method = "TMM") 233 | } 234 | if (method == "edgeR") { 235 | # design <- stats::model.matrix(~ group) 236 | design <- stats::model.matrix(~ d.mont$sample$group) 237 | if (min(table(d.mont$sample$group)) > 1) { 238 | d.mont <- edgeR::estimateDisp(d.mont, design) |> 239 | edgeR::estimateGLMCommonDisp(design = design) 240 | DEGAll <- edgeR::glmQLFit(d.mont, design = design) 241 | DEGAll_table <- NULL 242 | if (useTopconfects) { 243 | DEGAll_table <- topconfects::edger_confects(DEGAll, 244 | fdr = 0.05, 245 | coef = ncol(DEGAll$design), 246 | step = 0.05 247 | )$table 248 | rownames(DEGAll_table) <- DEGAll_table$name 249 | } 250 | # edgeR::topTags(n = nrow(d.mont$counts)) |> 251 | DEGAll <- DEGAll |> 252 | edgeR::glmQLFTest(coef = ncol(DEGAll$design)) |> 253 | edgeR::topTags(n = Inf, adjust.method = adjust.method) |> 254 | as.data.frame() |> 255 | rename(c("FDR" = "adj.P.Val")) |> 256 | rename(c("PValue" = "P.Value")) 257 | if (!is.null(DEGAll_table)) { 258 | genes <- intersect(rownames(DEGAll), rownames(DEGAll_table)) 259 | DEGAll <- cbind(DEGAll[genes, ], DEGAll_table[genes, ]) 260 | DEGAll <- DEGAll[order(DEGAll$P.Value), ] 261 | } 262 | } else { 263 | DEGAll <- edgeR::glmFit(d.mont, dispersion = 0) 264 | DEGAll <- DEGAll |> 265 | edgeR::glmLRT(coef = ncol(DEGAll$design)) |> 266 | edgeR::topTags(n = Inf, adjust.method = adjust.method) |> 267 | as.data.frame() |> 268 | rename(c("FDR" = "adj.P.Val")) |> 269 | rename(c("PValue" = "P.Value")) 270 | } 271 | } 272 | 273 | if (method == "limma") { 274 | comparison <- paste(unique(group), collapse = "-") 275 | group <- factor(group) 276 | design <- stats::model.matrix(~ 0 + group) 277 | colnames(design) <- levels(group) 278 | contrast.matrix <- limma::makeContrasts( 279 | contrasts = comparison, 280 | levels = design 281 | ) 282 | DEGAll <- limma::voom(d.mont, design = design, plot = FALSE) |> 283 | limma::lmFit(design) 284 | DEGAll_table <- NULL 285 | if (useTopconfects) { 286 | DEGAll_table <- topconfects::limma_confects(DEGAll, 287 | coef = 1, 288 | fdr = 0.05 289 | )$table 290 | rownames(DEGAll_table) <- DEGAll_table$name 291 | } 292 | DEGAll <- DEGAll |> 293 | limma::contrasts.fit(contrast.matrix) |> 294 | limma::eBayes() |> 295 | limma::topTable(number = Inf, adjust.method = adjust.method) 296 | if (!is.null(DEGAll_table)) { 297 | genes <- intersect(rownames(DEGAll), rownames(DEGAll_table)) 298 | DEGAll <- cbind(DEGAll[genes, ], DEGAll_table[genes, ]) 299 | DEGAll <- DEGAll[order(DEGAll$P.Value), ] 300 | } 301 | } 302 | 303 | if (method == "dearseq") { 304 | group[group == unique(group)[1]] <- 1 305 | group[group == unique(group)[2]] <- 2 306 | conditions <- matrix(as.numeric(group), ncol = 1) 307 | dearseqTest <- "asymptotic" 308 | if (edgeRNorm) { 309 | count_norm <- edgeR::cpm(d.mont, log = TRUE) 310 | DEGAll <- dearseq::dear_seq( 311 | exprmat = count_norm, variables2test = conditions, 312 | which_test = dearseqTest, parallel_comp = FALSE, 313 | preprocessed = TRUE 314 | ) 315 | } else { 316 | DEGAll <- dearseq::dear_seq( 317 | exprmat = as.matrix(counts), variables2test = conditions, 318 | which_test = dearseqTest, parallel_comp = FALSE, 319 | preprocessed = FALSE, 320 | padjust_methods = adjust.method 321 | ) 322 | } 323 | DEGAll <- DEGAll$pvals |> 324 | rename(c("adjPval" = "adj.P.Val")) |> 325 | rename(c("rawPval" = "P.Value")) 326 | } 327 | 328 | if (method == "Wilcoxon") { 329 | count_norm <- edgeR::cpm(d.mont, log = TRUE) |> as.data.frame() 330 | pvalues <- rep(0, nrow(count_norm)) 331 | 332 | count_disease <- as.matrix(count_norm[, group == unique(group)[1]]) 333 | count_normal <- as.matrix(count_norm[, group == unique(group)[2]]) 334 | for (i in seq_len(length(pvalues))) { 335 | pvalues[i] <- stats::wilcox.test(count_disease[i, ], 336 | count_normal[i, ])$p.value 337 | } 338 | fdr <- stats::p.adjust(pvalues, method = adjust.method) 339 | DEGAll <- data.frame(P.Value = pvalues, adj.P.Val = fdr) 340 | rownames(DEGAll) <- rownames(count_norm) 341 | } 342 | 343 | if (method == "NOISeq") { 344 | conditions <- factor(group) 345 | data <- NOISeq::readData(data = counts, 346 | factors = as.data.frame(conditions)) 347 | res <- NOISeq::noiseqbio(data, 348 | k = 0.5, norm = "tmm", factor = "conditions", 349 | random.seed = 12345, filter = 1, cv.cutoff = 100, cpm = 1 350 | ) 351 | DEGAll <- NOISeq::degenes(res, q = 0, M = NULL) |> 352 | rename(c("prob" = "P.Value")) 353 | DEGAll$adj.P.Val <- DEGAll$P.Value 354 | } 355 | } 356 | if ("P.Value" %in% colnames(DEGAll)) { 357 | DEGAll <- DEGAll[!is.na(DEGAll[, "P.Value"]), ] 358 | } 359 | 360 | return(DEGAll) 361 | } 362 | -------------------------------------------------------------------------------- /R/SNP.R: -------------------------------------------------------------------------------- 1 | #' Do difference analysis of SNP data 2 | #' 3 | #' @param snpDf data.frame of SNP data, each column is a sample, 4 | #' and each row is a SNP. 5 | #' @param sampleGroup vector of sample group. 6 | #' @param combineMethod Method of combining the 7 | #' pvalue of multiple snp in a gene. 8 | #' @return data.frame 9 | #' @export 10 | #' 11 | #' @examples 12 | #' \donttest{ 13 | #' library(TCGAbiolinks) 14 | #' query <- GDCquery( 15 | #' project = "TCGA-CHOL", 16 | #' data.category = "Simple Nucleotide Variation", 17 | #' access = "open", 18 | #' legacy = FALSE, 19 | #' data.type = "Masked Somatic Mutation", 20 | #' workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking" 21 | #' ) 22 | #' GDCdownload(query) 23 | #' data_snp <- GDCprepare(query) 24 | #' samples <- unique(data_snp$Tumor_Sample_Barcode) 25 | #' sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE) 26 | #' names(sampleGroup) <- samples 27 | #' pvalue <- differential_SNP_tcga(snpData = data_snp, 28 | #' sampleGroup = sampleGroup) 29 | #' } 30 | #' # use demo data 31 | #' snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) 32 | #' snpDf <- as.data.frame(snpDf) 33 | #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 34 | #' result <- differential_SNP(snpDf, sampleGroup) 35 | differential_SNP <- function(snpDf, sampleGroup, combineMethod = min) { 36 | snpDf[!is.na(snpDf)] <- "mutation" 37 | snpDf[is.na(snpDf)] <- "wild" 38 | sampleGroup <- sampleGroup[!is.na(sampleGroup)] 39 | type1 <- which(sampleGroup == names(table(sampleGroup))[1]) 40 | type2 <- which(sampleGroup == names(table(sampleGroup))[2]) 41 | pvalue <- rep(0, nrow(snpDf)) 42 | estimate <- rep(0, nrow(snpDf)) 43 | for (i in seq_len(nrow(snpDf))) { 44 | type1_freq <- table(as.character(snpDf[i, type1])) 45 | type2_freq <- table(as.character(snpDf[i, type2])) 46 | df <- data.frame( 47 | type1 = as.numeric(type1_freq[c("wild", "mutation")]), 48 | type2 = as.numeric(type2_freq[c("wild", "mutation")]) 49 | ) 50 | df[is.na(df)] <- 0 51 | fish <- stats::fisher.test(df) 52 | pvalue[i] <- fish$p.value 53 | estimate[i] <- fish$estimate 54 | } 55 | names(pvalue) <- names(estimate) <- sub("_.*", "", rownames(snpDf)) 56 | if (!is.null(combineMethod)) { 57 | pvalue <- stats::aggregate(pvalue, by = list(names(pvalue)), 58 | FUN = combineMethod) 59 | estimate <- stats::aggregate(estimate, 60 | by = list(names(estimate)), FUN = mean) 61 | return(data.frame(gene = pvalue[, 1], pvalue = pvalue[, 2], 62 | estimate = estimate[, 2])) 63 | } else { 64 | return(data.frame(pvalue = pvalue, estimate = estimate)) 65 | } 66 | } 67 | 68 | #' combine pvalues of SNP difference analysis result 69 | #' 70 | #' @param snpResult data.frame of SNP difference analysis result. 71 | #' @param snp2gene data frame of two column: snp and gene. 72 | #' @param combineMethod Method of combining the 73 | #' pvalue of multiple snp in a gene. 74 | #' @return data.frame 75 | #' @export 76 | #' @examples 77 | #' snpResult <- data.frame(pvalue = runif(100), estimate = runif(100)) 78 | #' rownames(snpResult) <- paste0("snp", seq_len(100)) 79 | #' snp2gene <- data.frame(snp = rownames(snpResult), 80 | #' gene = rep(paste0("gene", seq_len(20)), 5)) 81 | #' result <- combine_pvalue(snpResult, snp2gene) 82 | combine_pvalue <- function(snpResult, snp2gene, combineMethod = min) { 83 | pvalue <- snpResult$pvalue 84 | estimate <- snpResult$estimate 85 | genes <- snp2gene[, 2] 86 | names(genes) <- snp2gene[, 1] 87 | snps <- rownames(snpResult) 88 | names(pvalue) <- names(estimate) <- genes[snps] 89 | pvalue <- stats::aggregate(pvalue, by = list(names(pvalue)), 90 | FUN = combineMethod) 91 | estimate <- stats::aggregate(estimate, by = list(names(estimate)), 92 | FUN = mean) 93 | return(data.frame(gene = pvalue[, 1], pvalue = pvalue[, 2], 94 | estimate = estimate[, 2])) 95 | 96 | } 97 | 98 | #' Do difference analysis of SNP data downloaded from TCGAbiolinks 99 | #' 100 | #' @param snpData data.frame of SNP data downloaded from TCGAbiolinks 101 | #' @param sampleGroup vector of sample group 102 | #' @param combineMethod Method of combining the pvalue of 103 | #' multiple snp in a gene. 104 | #' @return data.frame 105 | #' @export 106 | #' 107 | #' @examples 108 | #' \donttest{ 109 | #' library(TCGAbiolinks) 110 | #' query <- GDCquery( 111 | #' project = "TCGA-CHOL", 112 | #' data.category = "Simple Nucleotide Variation", 113 | #' access = "open", 114 | #' legacy = FALSE, 115 | #' data.type = "Masked Somatic Mutation", 116 | #' workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking" 117 | #' ) 118 | #' GDCdownload(query) 119 | #' data_snp <- GDCprepare(query) 120 | #' samples <- unique(data_snp$Tumor_Sample_Barcode) 121 | #' sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE) 122 | #' names(sampleGroup) <- samples 123 | #' pvalue <- differential_SNP_tcga(snpData = data_snp, 124 | #' sampleGroup = sampleGroup) 125 | #' } 126 | #' # use demo data 127 | #' snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) 128 | #' snpDf <- as.data.frame(snpDf) 129 | #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 130 | #' result <- differential_SNP(snpDf, sampleGroup) 131 | differential_SNP_tcga <- function(snpData, sampleGroup, combineMethod = NULL) { 132 | Tumor_Sample_Barcode <- Variant_Classification <- NULL 133 | snpName <- paste(snpData$Hugo_Symbol, snpData$Start_Position, sep = "_") 134 | # snpData <- snpData[, c("Hugo_Symbol", "Start_Position", "Chromosome", 135 | # "Variant_Classification", "Tumor_Sample_Barcode", 136 | # "Variant_Type", "dbSNP_RS", "Mutation_Status", 137 | # # "MAX_AF", 138 | # "Reference_Allele", "Tumor_Seq_Allele1", "Tumor_Seq_Allele2", 139 | # "Match_Norm_Validation_Allele1", "Match_Norm_Validation_Allele2")] 140 | snpData <- snpData[, c("Variant_Classification", "Tumor_Sample_Barcode")] 141 | snpData$snp <- snpName 142 | snpData <- tidyr::spread(snpData, Tumor_Sample_Barcode, 143 | Variant_Classification) 144 | snpData <- as.data.frame(snpData) 145 | i <- match(colnames(snpData), names(sampleGroup)) 146 | sampleGroup <- sampleGroup[i] 147 | rownames(snpData) <- snpData$snp 148 | snpData <- snpData[, -1] 149 | pvalue <- differential_SNP(snpDf = snpData, sampleGroup = sampleGroup, 150 | combineMethod = combineMethod) 151 | return(pvalue) 152 | } 153 | 154 | #' Do difference analysis of SNP data downloaded from GEO 155 | #' 156 | #' @param snpData data.frame of SNP data downloaded from GEO 157 | #' @param sampleGroup vector of sample group 158 | #' @param method one of "Chisquare", "fisher", 159 | #' and "CATT"(Cochran-Armitage trend test) 160 | #' @return data.frame 161 | #' @export 162 | #' @examples 163 | #' \donttest{ 164 | #' file1 <- read.table("GSE66903_series_matrix.txt.gz", 165 | #' fill=TRUE, comment.char="!", header = TRUE) 166 | #' rownames(file1) <- file1[, 1] 167 | #' snpData <- file1[, -1] 168 | #' sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE) 169 | #' names(sampleGroup) <- colnames(snpData) 170 | #' snpData <- SNP_QC(snpData) 171 | #' sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE) 172 | #' result1 <- differential_SNP_GEO(snpData = snpData, 173 | #' sampleGroup = sampleGroup, method = "Chisquare") 174 | #' } 175 | #' # use demo data 176 | #' snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) 177 | #' snpDf <- as.data.frame(snpDf) 178 | #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 179 | #' result <- differential_SNP_GEO(snpDf, sampleGroup, method = "fisher") 180 | differential_SNP_GEO <- function(snpData, sampleGroup, method = "Chisquare") { 181 | snpDf <- as.matrix(snpData) 182 | sampleGroup <- sampleGroup[!is.na(sampleGroup)] 183 | type1 <- which(sampleGroup == names(table(sampleGroup))[1]) 184 | type2 <- which(sampleGroup == names(table(sampleGroup))[2]) 185 | pvalue <- rep(1, nrow(snpDf)) 186 | estimate <- rep(0, nrow(snpDf)) 187 | for (i in seq_len(nrow(snpDf))) { 188 | type1_freq <- table(snpDf[i, type1]) 189 | type2_freq <- table(snpDf[i, type2]) 190 | types <- unique(snpDf[i, ]) 191 | df <- data.frame( 192 | type1_freq = as.numeric(type1_freq[types]), 193 | type2_freq = as.numeric(type2_freq[types]) 194 | ) 195 | df[is.na(df)] <- 0 196 | if (nrow(df) > 2) { 197 | if (method == "fisher") { 198 | fish <- stats::fisher.test(df) 199 | pvalue[i] <- fish$p.value 200 | if (nrow(df) == 2) { 201 | estimate[i] <- fish$estimate 202 | } 203 | } 204 | 205 | if (method == "Chisquare") { 206 | pvalue[i] <- stats::chisq.test(df)$p.value 207 | } 208 | 209 | if(method == "CATT") { 210 | pvalue[i] <- CATT::CATT(table = t(df))$p.value 211 | } 212 | } 213 | 214 | } 215 | names(pvalue) <- names(estimate) <- rownames(snpDf) 216 | 217 | return(data.frame(pvalue = pvalue, estimate = estimate)) 218 | } 219 | 220 | 221 | get_maf <- function(x) { 222 | x <- x[x != "NoCall"] 223 | freq <- strsplit(x, split = "") |> unlist() |> table() 224 | min(freq) / sum(freq) 225 | } 226 | 227 | get_hwe <- function(x) { 228 | x <- x[x != "NoCall"] 229 | aa <- table(x) 230 | table_x <- as.numeric(aa) 231 | names(table_x) <- names(aa) 232 | # table_x <- table_x[sort(names(table_x))] 233 | freq <- strsplit(x, split = "") |> unlist() |> table() 234 | # freq <- freq[sort(names(freq))] 235 | table_y <- rep(0, 3) 236 | 237 | 238 | names(table_y) <- names(table_x) 239 | freq1 <- freq[1]/ sum(freq) 240 | freq2 <- freq[2]/ sum(freq) 241 | sum_freq <- length(x) 242 | table_y[paste0(names(freq)[1], names(freq)[1])] <- freq1 * freq1 * sum_freq 243 | if (length(table_x) > 1) { 244 | table_y[paste0(names(freq)[1], 245 | names(freq)[2])] <- freq1 * freq2 * sum_freq * 2 246 | table_y[paste0(names(freq)[2], 247 | names(freq)[1])] <- freq1 * freq2 * sum_freq * 2 248 | } 249 | if (length(table_x) > 2) { 250 | table_y[paste0(names(freq)[2], 251 | names(freq)[2])] <- freq2 * freq2 * sum_freq 252 | } 253 | df <- data.frame(table_x, table_y[names(table(x))]) 254 | stats::chisq.test(df)$p.value 255 | } 256 | 257 | #' Do quality control of SNP data downloaded from TCGAbiolinks 258 | #' 259 | #' @param snpData data.frame of SNP data downloaded from TCGAbiolinks 260 | #' @param geon filters out all variants with missing call rates 261 | #' exceeding the provided value (default 0.02) to be removed 262 | #' @param mind filters out all samples with missing call rates exceeding 263 | #' the provided value (default 0.02) to be removed 264 | #' @param maf filters out all variants with minor allele frequency below 265 | #' the provided threshold 266 | #' @param hwe filters out all variants which have Hardy-Weinberg 267 | #' equilibrium exact test p-value below the provided threshold 268 | #' @param miss character of miss value 269 | #' @return data.frame 270 | #' @export 271 | #' @examples 272 | #' # use demo data 273 | #' snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) 274 | #' snpDf <- as.data.frame(snpDf) 275 | #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 276 | #' result <- SNP_QC(snpDf) 277 | SNP_QC <- function(snpData, geon = 0.02, mind = 0.02, maf = 0.05, 278 | hwe = 1e-6, miss = "NoCall") { 279 | snpData_mat <- as.matrix(snpData) 280 | ## filter by 0.2 281 | aa <- snpData_mat |> apply(MARGIN = 2, FUN = function(x) { 282 | table(x)[miss] / length(x) 283 | } 284 | ) 285 | 286 | aa[is.na(aa)] <- 0 287 | snpData_mat <- snpData_mat[, aa < 0.2] 288 | 289 | bb <- snpData_mat |> apply(MARGIN = 1, FUN = function(x) { 290 | table(x)[miss] / length(x) 291 | } 292 | ) 293 | 294 | bb[is.na(bb)] <- 0 295 | snpData_mat <- snpData_mat[bb < 0.2, ] 296 | 297 | ## filter by cutoff 298 | aa <- snpData_mat |> apply(MARGIN = 2, FUN = function(x) 299 | {table(x)[miss] / length(x)}) 300 | 301 | aa[is.na(aa)] <- 0 302 | snpData_mat <- snpData_mat[, aa < mind] 303 | 304 | bb <- snpData_mat |> apply(MARGIN = 1, FUN = function(x) 305 | {table(x)[miss] / length(x)}) 306 | 307 | bb[is.na(bb)] <- 0 308 | snpData_mat <- snpData_mat[bb < geon, ] 309 | 310 | ## maf 311 | MAF <- snpData_mat |> apply(MARGIN = 1, FUN = get_maf) 312 | snpData_mat <- snpData_mat[MAF > maf, ] 313 | HWE <- snpData_mat |> apply(MARGIN = 1, FUN = get_hwe) 314 | snpData_mat <- snpData_mat[HWE > hwe, ] |> as.data.frame() 315 | return(snpData_mat) 316 | } 317 | 318 | 319 | -------------------------------------------------------------------------------- /R/TCGA_id_conversion.R: -------------------------------------------------------------------------------- 1 | #' Convert ENSEMBL gene id to gene Symbol in TCGA 2 | #' 3 | #' @param profiles a data.frame of gene expression data, 4 | #' each column is a sample, 5 | #' and each row is a gene. 6 | #' @param toType one of 'keytypes(org.Hs.eg.db)' 7 | #' 8 | #' @return a data.frame, gene symbols and their expression value 9 | #' @export 10 | #' 11 | #' @examples 12 | #' library(org.Hs.eg.db) 13 | #' data(profile) 14 | #' result <- id_conversion_TCGA(profile) 15 | id_conversion_TCGA <- function(profiles, toType = "SYMBOL") { 16 | rownames(profiles) <- gsub("\\..*", "", rownames(profiles)) 17 | genes <- clusterProfiler::bitr(rownames(profiles), 18 | fromType = "ENSEMBL", 19 | toType = toType, OrgDb = org.Hs.eg.db::org.Hs.eg.db, drop = FALSE 20 | ) 21 | 22 | genes <- genes[!duplicated(genes[, 1]), ] 23 | rownames(genes) <- genes[, 1] 24 | profiles2 <- as.matrix(profiles) 25 | rownames(profiles2) <- genes[rownames(profiles), 2] 26 | return(profiles2) 27 | } 28 | -------------------------------------------------------------------------------- /R/arrayDiff.R: -------------------------------------------------------------------------------- 1 | #' Differential analysis of Microarray data 2 | #' 3 | #' @param df data.frame of the omic data, each column is a sample, 4 | #' and each row is a gene. 5 | #' @param group a vector, group of samples. 6 | #' @param method method to do differential analysis, 7 | #' one of "limma", "ttest", "wilcox". 8 | #' @param adjust.method adjust.method, one of "holm", "hochberg", "hommel", 9 | #' "bonferroni", "BH", "BY", "fdr", and "none". 10 | #' @return data.frame 11 | #' @export 12 | #' 13 | #' @examples 14 | #' \donttest{ 15 | #' library(GeoTcgaData) 16 | #' library(data.table) 17 | #' # Use real GEO data as example 18 | #' arrayData <- read.table("GSE54807_series_matrix.txt.gz", 19 | #' sep = "\t", header = TRUE, 20 | #' fill=TRUE, comment.char = "!", check.names=FALSE) 21 | #' gpl <- fread("GPL6244-17930.txt", sep = "\t", header = TRUE) 22 | #' gpl <- gpl[, c("ID", "gene_assignment")] 23 | #' class(gpl) <- "data.frame" 24 | #' 25 | #' for (i in seq_len(nrow(gpl))) { 26 | #' aa <- strsplit(gpl[i, 2], " // ")[[1]][5] 27 | #' gpl[i, 2] <- as.character(strsplit(aa, " /// ")[[1]][1]) 28 | #' } 29 | #' gpl[,1] <- as.character(gpl[,1]) 30 | #' arrayData[, 1] <- as.character(arrayData[, 1]) 31 | #' rownames(gpl) <- gpl[, 1] 32 | #' arrayData[, 1] <- gpl[arrayData[, 1], 2] 33 | #' 34 | #' 35 | #' arrayData <- repRemove(arrayData," /// ") 36 | #' 37 | #' # Remove rows that do not correspond to genes 38 | #' arrayData <- arrayData[!is.na(arrayData[, 1]), ] 39 | #' arrayData <- arrayData[!arrayData[, 1] == "", ] 40 | #' arrayData <- arrayData[!arrayData[, 1] == "---", ] 41 | #' 42 | #' 43 | #' arrayData <- arrayData[order(arrayData[, 1]), ] 44 | #' arrayData <- gene_ave(arrayData, 1) 45 | #' 46 | #' keep <- apply(arrayData, 1, function(x) sum(x < 1) < (length(x)/2)) 47 | #' arrayData <- arrayData[keep, ] 48 | #' 49 | #' group <- c(rep("group1", 12), rep("group2", 12)) 50 | #' result <- differential_array(df = arrayData, group = group) 51 | #' } 52 | #' # Use random data as example 53 | #' arrayData <- matrix(runif(200), 25, 8) 54 | #' rownames(arrayData) <- paste0("gene", 1:25) 55 | #' colnames(arrayData) <- paste0("sample", 1:8) 56 | #' group <- c(rep("group1", 4), rep("group2", 4)) 57 | #' result <- differential_array(df = arrayData, group = group) 58 | differential_array <- function(df, group, method = "limma", 59 | adjust.method = "BH") { 60 | method <- match.arg(method, c("limma", "ttest", "wilcox")) 61 | if (method == "limma") { 62 | result <- differential_limma(df, group, adjust.method = adjust.method) 63 | } else { 64 | groups <- unique(group) 65 | which1 <- which(group == groups[1]) 66 | which2 <- which(group == groups[2]) 67 | P.Value <- rep(0, nrow(df)) 68 | if (method == "ttest") { 69 | for (i in seq_len(length(P.Value))) { 70 | P.Value[i] <- stats::t.test(df[i, which1], 71 | df[i, which2])$p.value 72 | } 73 | } else { 74 | for (i in seq_len(length(P.Value))) { 75 | P.Value[i] <- stats::wilcox.test(as.numeric(df[i, which1]), 76 | as.numeric(df[i, which2]))$p.value 77 | } 78 | } 79 | adj.P.Val <- stats::p.adjust(P.Value, method = adjust.method) 80 | result <- data.frame(gene = rownames(df), 81 | P.Value = P.Value, adj.P.Val = adj.P.Val) 82 | } 83 | return(result) 84 | } 85 | 86 | 87 | #' Get Microarray matrix data from GEO 88 | #' 89 | #' @param gse GSE number, such as GSE781. 90 | #' 91 | #' @return a list of matrix 92 | #' @export 93 | #' 94 | #' @examples 95 | #' \donttest{ 96 | #' arraylist <- get_geo_array("GSE781") 97 | #' } 98 | get_geo_array <- function(gse) { 99 | gse <- GEOquery::getGEO(gse, GSEMatrix = FALSE, AnnotGPL = TRUE) 100 | gselist <- vector("list", length(GEOquery::GPLList(gse))) 101 | names(gselist) <- names(GEOquery::GPLList(gse)) 102 | gsmplatforms <- lapply(GEOquery::GSMList(gse), 103 | function(x) {GEOquery::Meta(x)$platform_id}) 104 | for (i in seq_len(length(gselist))) { 105 | gsmlist <- BiocGenerics::Filter(function(gsm) { 106 | GEOquery::Meta(gsm)$platform_id==names(gselist)[i]}, 107 | GEOquery::GSMList(gse)) 108 | probesets <- GEOquery::Table(GEOquery::GPLList(gse)[[1]])$ID 109 | data.matrix <- do.call('cbind', 110 | lapply(gsmlist, function(x) {tab <- GEOquery::Table(x) 111 | mymatch <- match(probesets,tab$ID_REF) 112 | return(tab$VALUE[mymatch]) 113 | } 114 | ) 115 | ) 116 | data.matrix <- apply(data.matrix,2, 117 | function(x) {as.numeric(as.character(x))}) 118 | gpl <- gse@gpls[names(gselist)[i]] 119 | gpl <- gpl[[1]]@dataTable@table 120 | genes <- gpl[match(probesets, gpl[, "ID"]), "Gene Symbol"] 121 | rownames(data.matrix) <- genes 122 | colnames(data.matrix) <- names(gsmlist) 123 | gselist[[i]] <- data.matrix 124 | } 125 | } 126 | 127 | #' Preprocess of Microarray data 128 | #' 129 | #' @param x matrix of Microarray data, each column is a sample, 130 | #' and each row is a gene. 131 | #' @param missing_value Method to impute missing expression data, 132 | #' one of "zero" and "knn". 133 | #' @param string a string, sep of the gene 134 | #' 135 | #' @return matrix 136 | #' @export 137 | #' 138 | #' @examples 139 | #' \donttest{ 140 | #' arraylist <- get_geo_array("GSE781") 141 | #' arraylist <- lapply(arraylist, array_preprocess) 142 | #' } 143 | array_preprocess <- function(x, missing_value = "knn", string = " /// ") { 144 | ## filter 145 | x <- x[!is.na(rownames(x)), ] 146 | x <- x[rownames(x) != "", ] 147 | aa <- rowSums(is.na(x)) 148 | x <- x[aa < ncol(x)/2, ] 149 | 150 | ## impute missing 151 | if (missing_value == "zero") { 152 | x[is.na(x)] <- 0 153 | } else { 154 | x <- impute::impute.knn(x)$data 155 | } 156 | 157 | ## log 158 | qx <- as.numeric(stats::quantile(x, 159 | c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm = TRUE)) 160 | LogC <- (qx[5] > 100) || (qx[6]-qx[1] > 50 && qx[2] > 0) 161 | if (LogC) { 162 | x[which(x <= 0)] <- 0.0001 163 | x <- log2(x) 164 | } 165 | 166 | ## gene id conversion 167 | x <- cbind(rownames(x), x) 168 | x <- repAssign(x, string) 169 | # gene_ave(x) 170 | } 171 | 172 | #' cluster probes of Microarray data 173 | #' 174 | #' @param x matrix of Microarray data, the first is the name of the gene, 175 | #' and the others are the expression value. 176 | #' @param clusterCutoff Pearson correlation threshold 177 | #' to cut off the hierarchical tree. 178 | #' @importFrom stats as.dist 179 | #' @importFrom stats cor 180 | #' @importFrom stats cutree 181 | #' @importFrom stats hclust 182 | #' @return data.frame 183 | #' @export 184 | #' 185 | #' @examples 186 | #' \donttest{ 187 | #' arraylist <- get_geo_array("GSE781") 188 | #' arraylist <- lapply(arraylist, array_preprocess) 189 | #' arraylist_cluster <- lapply(arraylist, cluster_array) 190 | #' } 191 | cluster_array <- function(x, clusterCutoff = 0.7) { 192 | genes <- x[, 1] 193 | uniqueGenes <- unique(genes) 194 | x <- x[, -1] 195 | matlist <- vector("list", length(uniqueGenes)) 196 | for (i in seq_len(length(uniqueGenes))) { 197 | gene <- uniqueGenes[i] 198 | probes <- which(genes == gene) 199 | mat <- x[probes, ] 200 | if (length(probes) == 1) { 201 | rownames(mat) <- gene 202 | matlist[[i]] <- mat 203 | } else { 204 | probeCorrelation <- cor(t(mat),method = 'pearson') 205 | ClusterResults <- hclust(as.dist(1-probeCorrelation), 206 | method = "complete", members = NULL) 207 | #plot(ClusterResults) 208 | Clusters <- cutree(ClusterResults, h = clusterCutoff) 209 | clusterDf <- matrix(0, length(unique(Clusters)), ncol(mat)) |> 210 | as.data.frame() 211 | for (j in seq_len(length(unique(Clusters)))) { 212 | tmpGeneProbes <- which(Clusters == j) 213 | if (length(tmpGeneProbes) > 1) { 214 | clusterDf[j, ] <- colMeans(mat[tmpGeneProbes,]) 215 | } else { 216 | clusterDf[j, ] <- mat[tmpGeneProbes,] 217 | } 218 | } 219 | if (nrow(clusterDf) > 1) { 220 | rownames(clusterDf) <- 221 | paste(gene, seq(nrow(clusterDf)), sep = "_") 222 | } else { 223 | rownames(clusterDf) <- gene 224 | } 225 | 226 | colnames(clusterDf) <- colnames(mat) 227 | matlist[[i]] <- clusterDf 228 | } 229 | } 230 | matlist <- do.call("rbind", matlist) 231 | } -------------------------------------------------------------------------------- /R/calculate_mean_module.R: -------------------------------------------------------------------------------- 1 | #' Find the mean value of the gene in each module 2 | #' 3 | #' @param geneExpress a data.frame of gene expression data. 4 | #' Each column is a sample, and each row is a gene. 5 | #' @param module a data.frame of two column. The first column is module name, 6 | #' the second column are genes in this module. 7 | #' 8 | #' @return a data.frame, means the mean of gene expression value in 9 | #' the same module 10 | #' @export 11 | #' 12 | #' @examples 13 | #' data(geneExpress) 14 | #' data(module) 15 | #' result <- cal_mean_module(geneExpress, module) 16 | cal_mean_module <- function(geneExpress, module) { 17 | genes <- rownames(geneExpress) 18 | output_module <- matrix(0, nrow(module), 2) 19 | rownames(output_module) <- module[, 1] 20 | for (i in seq_len(nrow(module))) { 21 | modulen <- unlist(strsplit(module[i, 2], ",")) 22 | modulen <- intersect(modulen, genes) 23 | modulenDf <- geneExpress[modulen, ] 24 | output_module[i, ] <- colMeans(modulenDf) 25 | } 26 | as.data.frame(output_module) 27 | } 28 | -------------------------------------------------------------------------------- /R/calculate_mean_profile.R: -------------------------------------------------------------------------------- 1 | #' Average the values of same genes in gene expression profile 2 | #' 3 | #' @param file_gene_ave a data.frame of gene expression data, 4 | #' each column is a sample, and each row is a gene. 5 | #' @param k a number, indicates which is the gene column. 6 | #' 7 | #' @return a data.frame, the values of same genes in gene expression profile 8 | #' @export 9 | #' 10 | #' @examples 11 | #' aa <- c("MARCH1", "MARC1", "MARCH1", "MARCH1", "MARCH1") 12 | #' bb <- c(2.969058399, 4.722410064, 8.165514853, 8.24243893, 8.60815086) 13 | #' cc <- c(3.969058399, 5.722410064, 7.165514853, 6.24243893, 7.60815086) 14 | #' file_gene_ave <- data.frame(aa = aa, bb = bb, cc = cc) 15 | #' colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") 16 | #' 17 | #' result <- gene_ave(file_gene_ave, 1) 18 | gene_ave <- function(file_gene_ave, k = 1) { 19 | x <- file_gene_ave[, -k] 20 | file_gene_ave <- as.matrix(file_gene_ave) 21 | rownames(file_gene_ave) <- file_gene_ave[, k] 22 | # x <- file_gene_ave 23 | ID <- rownames(file_gene_ave) 24 | ID <- factor(ID, levels = unique(ID)) 25 | 26 | y <- rowsum(x, ID, reorder = FALSE, na.rm = TRUE) 27 | n <- rowsum(1L - is.na(x), ID, reorder = FALSE) 28 | return(y / n) 29 | } 30 | -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | 2 | #' a data.frame of gene expression data 3 | #' 4 | #' It is a randomly generated expression data 5 | #' used as an example of functions in this package. 6 | #' the rowname is gene symbols 7 | #' the columns are gene expression values 8 | #' 9 | #' @format A data.frame with 10779 rows and 2 column 10 | #' 11 | "geneExpress" 12 | 13 | # ' a matrix for Converting gene symbol to entrez_id or ensembl_gene_id 14 | # ' 15 | # ' the hgnc data comes from HGNC website 16 | # ' the columns represent "symbol", "locus_group", "locus_type", 17 | # ' "entrez_id" and "ensembl_gene_id" 18 | # ' 19 | # ' @format A matrix with 37647 rows and 5 column 20 | # ' 21 | "hgnc" 22 | 23 | #' a matrix of gene expression data in TCGA 24 | #' 25 | #' It is a randomly generated expression data 26 | #' used as an example of functions in this package. 27 | #' the first column represents the gene symbol 28 | #' 29 | #' the other columns represent the expression(FPKM) of genes 30 | #' 31 | #' @format A matrix with 10 rows and 10 column 32 | #' 33 | "profile" 34 | 35 | #' a matrix of gene expression data in GEO 36 | #' 37 | #' It is a randomly generated expression data 38 | #' used as an example of functions in this package. 39 | #' the first column represents the gene symbol 40 | #' 41 | #' the other columns represent the expression of genes 42 | #' 43 | #' @format A matrix with 32 rows and 20 column 44 | #' 45 | "ventricle" 46 | 47 | #' a matrix of gene expression data in TCGA 48 | #' 49 | #' It is a randomly generated expression data 50 | #' used as an example of functions in this package. 51 | #' the first column represents the gene symbol 52 | #' 53 | #' the other columns represent the expression(count) of genes 54 | #' 55 | #' @format A matrix with 100 rows and 150 column 56 | #' 57 | "kegg_liver" 58 | 59 | #' a matrix of gene expression data in GEO 60 | #' 61 | #' the first column represents the gene symbol 62 | #' 63 | #' the other columns represent the expression of genes 64 | #' 65 | #' @format A matrix with 999 rows and 3 column 66 | #' 67 | "GSE66705_sample2" 68 | 69 | #' a matrix of module name, gene symbols, and the number of gene symbols 70 | #' 71 | #' It is a randomly generated expression data 72 | #' used as an example of functions in this package. 73 | #' @format A matrix with 176 rows and 3 column 74 | #' 75 | "module" 76 | 77 | 78 | # ' a matrix for Converting gene symbol. 79 | # ' 80 | # ' the hgnc data comes from HGNC website 81 | # ' 82 | # ' @format A matrix with 43547 rows and 52 column 83 | # ' 84 | "hgnc_file" 85 | 86 | 87 | #' a data.frame of gene length and GC content 88 | #' 89 | #' the gene length and GC content data comes from 90 | #' TxDb.Hsapiens.UCSC.hg38.knownGene and 91 | #' BSgenome.Hsapiens.UCSC.hg38 92 | #' 93 | #' @format A data.frame with 27341 rows and 2 column 94 | #' 95 | "gene_cov" 96 | -------------------------------------------------------------------------------- /R/fpkm_count_conversion.r: -------------------------------------------------------------------------------- 1 | countToTpm_internal <- function(counts, effLen) { 2 | rate <- log(counts) - log(effLen) 3 | denom <- log(sum(exp(rate))) 4 | exp(rate - denom + log(1e6)) 5 | } 6 | countToFpkm_internal <- function(counts, effLen) { 7 | N <- sum(counts) 8 | exp(log(counts) + log(1e9) - log(effLen) - log(N)) 9 | } 10 | 11 | fpkmToTpm_internal <- function(fpkm) { 12 | exp(log(fpkm) - log(sum(fpkm)) + log(1e6)) 13 | } 14 | 15 | countToEffCounts_internal <- function(counts, len, effLen) { 16 | counts * (len / effLen) 17 | } 18 | 19 | # if we have fpkm, then we can easily get the rate of counts/sum(counts). 20 | # we can't get the real count value. 21 | fpkmToCount_internal <- function(fpkm, effLen, N = 1e9) { 22 | # rate <- (fpkm * effLen)/10^9 23 | rate <- exp(log(fpkm) + log(effLen) - log(1e9)) 24 | counts <- rate * N 25 | } 26 | 27 | 28 | 29 | 30 | #' Convert count to FPKM 31 | #' 32 | #' @param counts_matrix a matrix, colnames of counts_matrix are sample name, 33 | #' rownames of counts_matrix are gene symbols 34 | #' @param keyType keyType, one of keytypes(org.Hs.eg.db). 35 | #' @param gene_cov data.frame of two column, the first column is gene length, 36 | #' the second column is gene GC content 37 | #' 38 | #' @return a matrix 39 | #' @export 40 | #' 41 | #' @examples 42 | #' data(gene_cov) 43 | #' lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 44 | #' rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 45 | #' colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 46 | #' result <- countToFpkm(lung_squ_count2, 47 | #' keyType = "SYMBOL", 48 | #' gene_cov = gene_cov 49 | #' ) 50 | countToFpkm <- function(counts_matrix, keyType = "SYMBOL", gene_cov) { 51 | gene_cov2 <- gene_cov 52 | if (keyType != "ENTREZID") { 53 | genes_bitr <- clusterProfiler::bitr(rownames(gene_cov), 54 | fromType = "ENTREZID", toType = keyType, 55 | OrgDb = org.Hs.eg.db::org.Hs.eg.db, drop = TRUE 56 | ) 57 | genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ] 58 | gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] 59 | rownames(gene_cov2) <- genes_bitr[, 2] 60 | } 61 | genes_count <- intersect(rownames(counts_matrix), rownames(gene_cov2)) 62 | counts_matrix_new <- counts_matrix[genes_count, ] 63 | gene_loc_len_new <- gene_cov2[genes_count, ] 64 | genes_length <- as.numeric(gene_loc_len_new[, 1]) 65 | counts_matrix_new2 <- counts_matrix_new 66 | for (i in seq_len(dim(counts_matrix_new2)[2])) { 67 | counts_matrix_new2[, i] <- countToFpkm_internal( 68 | as.numeric(counts_matrix_new2[, i]), 69 | genes_length) 70 | } 71 | return(counts_matrix_new2) 72 | } 73 | 74 | 75 | #' Convert count to Tpm 76 | #' 77 | #' @param counts_matrix a matrix, colnames of counts_matrix are sample name, 78 | #' rownames of counts_matrix are gene symbols 79 | #' @param keyType keyType, one of keytypes(org.Hs.eg.db). 80 | #' @param gene_cov data.frame of two column, the first column is gene length, 81 | #' the second column is gene GC content 82 | #' 83 | #' @return a matrix 84 | #' @export 85 | #' 86 | #' @examples 87 | #' data(gene_cov) 88 | #' lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 89 | #' rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 90 | #' colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 91 | #' result <- countToTpm(lung_squ_count2, 92 | #' keyType = "SYMBOL", 93 | #' gene_cov = gene_cov 94 | #' ) 95 | countToTpm <- function(counts_matrix, keyType = "SYMBOL", gene_cov) { 96 | gene_cov2 <- gene_cov 97 | if (keyType != "ENTREZID") { 98 | genes_bitr <- clusterProfiler::bitr(rownames(gene_cov), 99 | fromType = "ENTREZID", toType = keyType, 100 | OrgDb = org.Hs.eg.db::org.Hs.eg.db, drop = TRUE 101 | ) 102 | genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ] 103 | gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] 104 | rownames(gene_cov2) <- genes_bitr[, 2] 105 | } 106 | genes_count <- intersect(rownames(counts_matrix), rownames(gene_cov2)) 107 | counts_matrix_new <- counts_matrix[genes_count, ] 108 | gene_loc_len_new <- gene_cov2[genes_count, ] 109 | genes_length <- as.numeric(gene_loc_len_new[, 1]) 110 | counts_matrix_new2 <- counts_matrix_new 111 | for (i in seq_len(dim(counts_matrix_new2)[2])) { 112 | counts_matrix_new2[, i] <- countToTpm_internal( 113 | as.numeric(counts_matrix_new2[, i]), 114 | genes_length) 115 | } 116 | return(counts_matrix_new2) 117 | } 118 | 119 | 120 | #' Convert fpkm to Tpm 121 | #' 122 | #' @param fpkm_matrix a matrix, colnames of fpkm_matrix are sample name, 123 | #' rownames of fpkm_matrix are genes 124 | #' 125 | #' @return a matrix 126 | #' @export 127 | #' 128 | #' @examples 129 | #' lung_squ_count2 <- matrix(c(0.11, 0.22, 0.43, 0.14, 0.875, 130 | #' 0.66, 0.77, 0.18, 0.29), ncol = 3) 131 | #' rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 132 | #' colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 133 | #' result <- fpkmToTpm(lung_squ_count2) 134 | fpkmToTpm <- function(fpkm_matrix) { 135 | fpkm_matrix_new <- apply(fpkm_matrix, 2, fpkmToTpm_internal) 136 | } 137 | -------------------------------------------------------------------------------- /R/metap.R: -------------------------------------------------------------------------------- 1 | # copy from metap package: https://CRAN.R-project.org/package=metap 2 | #' @importFrom stats na.fail 3 | #' @importFrom stats pchisq 4 | #' @importFrom stats pnorm 5 | #' @importFrom stats qnorm 6 | sumz <- function(p, weights = NULL, data = NULL, subset = NULL, 7 | na.action = na.fail, log.p = FALSE, log.input = FALSE) { 8 | if(is.null(data)) data <- sys.frame(sys.parent()) 9 | mf <- match.call() 10 | mf$data <- NULL 11 | mf$subset <- NULL 12 | mf$na.action <- NULL 13 | mf[[1]] <- as.name("data.frame") 14 | mf <- eval(mf, data) 15 | if(!is.null(subset)) mf <- mf[subset,] 16 | mf <- na.action(mf) 17 | p <- as.numeric(mf$p) 18 | weights <- mf$weights 19 | noweights <- is.null(weights) 20 | if(noweights) weights <- rep(1, length(p)) 21 | if(length(p) != length(weights)) warning("Length of p and weights differ") 22 | if(log.input) { 23 | keep <- p < 0 24 | } else { 25 | keep <- (p > 0) & (p < 1) 26 | } 27 | invalid <- sum(1L * keep) < 2 28 | if(invalid) { 29 | warning("Must have at least two valid p values") 30 | res <- list(z = NA_real_, p = NA_real_, 31 | validp = p[keep], weights = weights) 32 | } else { 33 | if(sum(1L * keep) != length(p)) { 34 | warning("Some studies omitted") 35 | omitw <- weights[!keep] 36 | if((sum(1L * omitw) > 0) & !noweights) 37 | warning("Weights omitted too") 38 | } 39 | zp <- (qnorm(p[keep], lower.tail = FALSE, log.p = log.input) %*% 40 | weights[keep]) / sqrt(sum(weights[keep]^2)) 41 | res <- list(z = zp, p = pnorm(zp, lower.tail = FALSE, 42 | log.p = log.p), 43 | validp = p[keep], weights = weights) 44 | } 45 | class(res) <- c("sumz", "metap") 46 | res 47 | } 48 | 49 | # copy from metap package: https://CRAN.R-project.org/package=metap 50 | print.sumz <- function(x, ...) { 51 | cat("sumz = ", x$z, "p = ", x$p, "\n") 52 | invisible(x) 53 | } 54 | 55 | # copy from metap package: https://CRAN.R-project.org/package=metap 56 | sumlog <- function(p, log.p = FALSE) { 57 | keep <- (p > 0) & (p <= 1) 58 | invalid <- sum(1L * keep) < 2 59 | if(invalid) { 60 | warning("Must have at least two valid p values") 61 | res <- list(chisq = NA_real_, df = NA_integer_, 62 | p = NA_real_, validp = p[keep]) 63 | } else { 64 | lnp <- log(p[keep]) 65 | chisq <- (-2) * sum(lnp) 66 | df <- 2 * length(lnp) 67 | if(length(lnp) != length(p)) { 68 | warning("Some studies omitted") 69 | } 70 | res <- list(chisq = chisq, df = df, 71 | p = pchisq(chisq, df, lower.tail = FALSE, 72 | log.p = log.p), validp = p[keep]) 73 | } 74 | class(res) <- c("sumlog", "metap") 75 | res 76 | } 77 | 78 | # copy from metap package: https://CRAN.R-project.org/package=metap 79 | print.sumlog <- function(x, ...) { 80 | cat("chisq = ", x$chisq, " with df = ", x$df, " p = ", x$p, "\n") 81 | invisible(x) 82 | } 83 | 84 | 85 | -------------------------------------------------------------------------------- /R/prepareChi.r: -------------------------------------------------------------------------------- 1 | #' Preparer file for chi-square test 2 | #' 3 | #' @param cnv result of ann_merge() 4 | #' 5 | #' @return a matrix 6 | #' @export 7 | #' 8 | #' @examples 9 | #' cnv <- matrix(c( 10 | #' -1.09150, -1.47120, -0.87050, -0.50880, 11 | #' -0.50880, 2.0, 2.0, 2.0, 2.0, 2.0, 2.601962, 2.621332, 2.621332, 12 | #' 2.621332, 2.621332, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 13 | #' 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0 14 | #' ), nrow = 5) 15 | #' cnv <- as.data.frame(cnv) 16 | #' rownames(cnv) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3") 17 | #' colnames(cnv) <- c( 18 | #' "TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01", 19 | #' "TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01", 20 | #' "TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01" 21 | #' ) 22 | #' cnv_chi_file <- prepare_chi(cnv) 23 | prepare_chi <- function(cnv) { 24 | file1 <- cnv 25 | sampless <- rep(0, ncol(file1)) 26 | for (i in seq_len(length(sampless))) 27 | { 28 | a <- unlist(strsplit(colnames(file1)[i], "-"))[4] 29 | sampless[i] <- substring(a, 1, 1) 30 | } 31 | 32 | cnv_chi <- matrix(0, nrow(cnv), 4) 33 | rownames(cnv_chi) <- rownames(cnv) 34 | colnames(cnv_chi) <- c("normalCNV", "normalWild", "tumorCNV", "tumorWild") 35 | for (i in seq_len(nrow(file1))) 36 | { 37 | normalCNV <- 0 38 | normalWild <- 0 39 | tumorCNV <- 0 40 | tumorWild <- 0 41 | for (j in seq_len(ncol(file1))) 42 | { 43 | if ((sampless[j] == "1") && 44 | (abs(as.numeric(file1[i, j]) - 2) > 0.5)) { 45 | normalCNV <- normalCNV + 1 46 | } 47 | if ((sampless[j] == "1") && 48 | (abs(as.numeric(file1[i, j]) - 2) <= 0.5)) { 49 | normalWild <- normalWild + 1 50 | } 51 | if ((sampless[j] == "0") && 52 | (abs(as.numeric(file1[i, j]) - 2) > 0.5)) { 53 | tumorCNV <- tumorCNV + 1 54 | } 55 | if ((sampless[j] == "0") && 56 | (abs(as.numeric(file1[i, j]) - 2) <= 0.5)) { 57 | tumorWild <- tumorWild + 1 58 | } 59 | } 60 | cnv_chi[i, ] <- c(normalCNV, normalWild, tumorCNV, tumorWild) 61 | } 62 | return(cnv_chi) 63 | } 64 | 65 | -------------------------------------------------------------------------------- /R/rep.R: -------------------------------------------------------------------------------- 1 | #' Handle the case where one id corresponds to multiple genes 2 | #' 3 | #' @param input_file input file, a data.frame or a matrix, 4 | #' the first column should be genes. 5 | #' @param string a string, sep of the gene 6 | #' 7 | #' @return a data.frame, when an id corresponds to multiple genes, 8 | #' the expression value is assigned to each gene 9 | #' @export 10 | #' 11 | #' @examples 12 | #' aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 13 | #' "MARCH3 /// MARCH4", "MARCH1") 14 | #' bb <- c("2.969058399", "4.722410064", "8.165514853", 15 | #' "8.24243893", "8.60815086") 16 | #' cc <- c("3.969058399", "5.722410064", "7.165514853", 17 | #' "6.24243893", "7.60815086") 18 | #' input_file <- data.frame(aa = aa, bb = bb, cc = cc) 19 | #' 20 | #' repAssign_result <- repAssign(input_file, " /// ") 21 | #' 22 | repAssign <- function(input_file, string) { 23 | name <- colnames(input_file)[1] 24 | genelist <- strsplit(input_file[, 1], string) 25 | geneLength <- unlist(lapply(genelist, length)) 26 | input_file <- input_file[, -1] 27 | output <- apply(input_file, 2, rep, times = geneLength) 28 | output2 <- matrix(as.numeric(output), nrow = nrow(output)) 29 | colnames(output2) <- colnames(output) 30 | output2 <- data.frame(unlist(genelist), output2, check.names = FALSE) 31 | colnames(output2)[1] <- name 32 | output2 33 | } 34 | 35 | #' Handle the case where one id corresponds to multiple genes 36 | #' 37 | #' @param input_file input file, a data.frame or a matrix, 38 | #' the first column should be genes. 39 | #' @param string a string,sep of the gene 40 | #' 41 | #' @return a data.frame, when an id corresponds to multiple genes, 42 | #' the expression value is deleted 43 | #' @export 44 | #' 45 | #' @examples 46 | #' aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 47 | #' "MARCH3 /// MARCH4", "MARCH1") 48 | #' bb <- c("2.969058399", "4.722410064", "8.165514853", 49 | #' "8.24243893", "8.60815086") 50 | #' cc <- c("3.969058399", "5.722410064", "7.165514853", 51 | #' "6.24243893", "7.60815086") 52 | #' input_file <- data.frame(aa = aa, bb = bb, cc = cc) 53 | #' repRemove_result <- repRemove(input_file, " /// ") 54 | repRemove <- function(input_file, string) { 55 | unKeep <- grep(string, input_file[, 1]) 56 | if (length(unKeep) > 0) input_file <- input_file[-unKeep, ] 57 | input_file 58 | } 59 | -------------------------------------------------------------------------------- /R/sysdata.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/R/sysdata.rda -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | --- 4 | 5 | 6 | 7 | ```{r, include = FALSE} 8 | knitr::opts_chunk$set( 9 | collapse = TRUE, 10 | comment = "#>", 11 | fig.path = "man/figures/", 12 | out.width = "100%" 13 | ) 14 | ``` 15 | 16 | # GeoTcgaData 17 | 18 | The goal of GeoTcgaData is to deal with RNA-seq, DNA Methylation, single nucleotide Variation and Copy number variation data in GEO and TCGA. 19 | 20 | ## :writing_hand: Authors 21 | Erqiang Hu 22 | 23 | Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University. 24 | 25 | 26 | ## :arrow\_double\_down: Installation 27 | 28 | 29 | ```{r eval=FALSE} 30 | if(!requireNamespace("devtools", quietly = TRUE)) 31 | install.packages("devtools") 32 | devtools::install_github("YuLab-SMU/GeoTcgaData") 33 | ``` 34 | 35 | ```{r} 36 | library(GeoTcgaData) 37 | ``` 38 | 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](http://xena.ucsc.edu/) , [UCSCXenaTools](https://cran.r-project.org/package=UCSCXenaTools)，and [sangerbox](http://vip.sangerbox.com/), but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data. 39 | 40 | ## Example 41 | 42 | This is a basic example which shows you how to solve a common problem: 43 | 44 | ## RNA-seq data differential expression analysis 45 | It is convenient to use [`TCGAbiolinks`](http://www.bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/analysis.html) or [`GDCRNATools`](https://bioconductor.org/packages/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). 46 | 47 | Alicia Oshlack et. al. claimed that unlike the chip data, the RNA-seq data had one [bias](https://pubmed.ncbi.nlm.nih.gov/20132535/): the larger the transcript length / mean read count , the more likely it was to be identified as a differential gene, [while there was no such trend in the chip data](https://pubmed.ncbi.nlm.nih.gov/19371405/). 48 | 49 | 50 | However, when we use their chip data for difference analysis( using the limma package), we find that chip data has the same trend as RNA-seq data. And we also found this trend in the difference analysis results given by the data [authors](https://genome.cshlp.org/content/18/9/1509.long). 51 | 52 | 53 | 54 | 55 | It is worse noting that [only technical replicate data, which has small gene dispersions, shows this bias](https://pubmed.ncbi.nlm.nih.gov/28545404/). This is because in technical replicate 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. However, we have not deduced why there is such a bias in the current difference analysis algorithms. 56 | 57 | Some software, such as [CQN](http://www.bioconductor.org/packages/cqn/) , present a [normalization algorithm](https://pubmed.ncbi.nlm.nih.gov/22285995/) to correct systematic biases(gene length bias and [GC-content bias](https://pubmed.ncbi.nlm.nih.gov/22177264/). But they did not provide sufficient evidence to prove that the correction is effective. We use the [Marioni dataset](https://pubmed.ncbi.nlm.nih.gov/19371405/) to verify the correction effect of CQN and find that there is still a deviation after correction: 58 | 59 | 60 | 61 | [GOseq](http://bioconductor.org/packages/goseq/) based on [Wallenius' noncentral hypergeometric distribution](https://en.wikipedia.org/wiki/Wallenius%27_noncentral_hypergeometric_distribution) can effectively correct the gene length deviation in enrichment analysis. However, the current RNA-seq data often have no gene length bias, but only the expression amount(read count) bias, GOseq may overcorrect these data, correcting originally unbiased data into reverse bias. 62 | 63 | 64 | GOseq also fails to correct for expression bias, therefore, read count bias correction is still a challenge for us. 65 | 66 | 67 | use `TCGAbiolinks` to download TCGA data 68 | 69 | ```{r eval=FALSE} 70 | # download RNA-seq data 71 | library(TCGAbiolinks) 72 | 73 | query <- GDCquery(project = "TCGA-ACC", 74 | data.category = "Transcriptome Profiling", 75 | data.type = "Gene Expression Quantification", 76 | workflow.type = "STAR - Counts") 77 | 78 | GDCdownload(query, method = "api", files.per.chunk = 3, 79 | directory = Your_Path) 80 | 81 | dataRNA <- GDCprepare(query = query, directory = Your_Path, 82 | save = TRUE, save.filename = "dataRNA.RData") 83 | ## get raw count matrix 84 | dataPrep <- TCGAanalyze_Preprocessing(object = dataRNA, 85 | cor.cut = 0.6, 86 | datatype = "STAR - Counts") 87 | 88 | ``` 89 | 90 | Use `differential_RNA` to do difference analysis. We provide the data of human gene length and GC content in `gene_cov`. 91 | 92 | ```{r eval=FALSE} 93 | group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE) 94 | library(cqn) # To avoid reporting errors: there is no function "rq" 95 | ## get gene length and GC content 96 | library(org.Hs.eg.db) 97 | genes_bitr <- bitr(rownames(gene_cov), fromType = "ENTREZID", toType = "ENSEMBL", 98 | OrgDb = org.Hs.eg.db, drop = TRUE) 99 | genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ] 100 | gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] 101 | rownames(gene_cov2) <- genes_bitr$ENSEMBL 102 | genes <- intersect(rownames(dataPrep), rownames(gene_cov2)) 103 | dataPrep <- dataPrep[genes, ] 104 | geneLength <- gene_cov2[genes, "length"] 105 | gccontent <- gene_cov2[genes, "GC"] 106 | names(geneLength) <- names(gccontent) <- genes 107 | ## Difference analysis 108 | DEGAll <- differential_RNA(counts = dataPrep, group = group, 109 | geneLength = geneLength, gccontent = gccontent) 110 | ``` 111 | 112 | Use `clusterProfiler` to do enrichment analytics: 113 | 114 | ```{r eval=FALSE} 115 | diffGenes <- DEGAll$logFC 116 | names(diffGenes) <- rownames(DEGAll) 117 | diffGenes <- sort(diffGenes, decreasing = TRUE) 118 | library(clusterProfiler) 119 | library(enrichplot) 120 | library(org.Hs.eg.db) 121 | gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL") 122 | dotplot(gsego) 123 | ``` 124 | 125 | 126 | 127 | ## DNA Methylation data integration 128 | use `TCGAbiolinks` to download TCGA data. 129 | 130 | The codes may need to be modified if `TCGAbiolinks` updates. So please read its [documents](https://www.bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html). 131 | 132 | ```{r eval=FALSE} 133 | library(TCGAbiolinks) 134 | query <- GDCquery(project = "TCGA-ACC", 135 | data.category = "DNA Methylation", 136 | data.type = "Methylation Beta Value", 137 | platform = "Illumina Human Methylation 450") 138 | GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path) 139 | ``` 140 | 141 | 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: 142 | 143 | ```{r eval=FALSE} 144 | merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data) 145 | ``` 146 | Then use differential_methy() to do difference analysis. 147 | 148 | ```{r eval=FALSE} 149 | # if (!requireNamespace("ChAMP", quietly = TRUE)) 150 | # BiocManager::install("ChAMP") 151 | library(ChAMP) # To avoid reporting errors 152 | differential_gene <- differential_methy(cpgData = merge_result, sampleGroup = sample(c("C","T"), 153 | ncol(merge_result[[1]]), replace = TRUE)) 154 | ``` 155 | 156 | **Note:** `ChAMP`has a large number of dependent packages. If you cannot install it successfully, you can download each dependent package separately(Source or Binary) and install it locally. 157 | 158 | 159 | 160 | If your methylation data was downloaded from [UCSC Xena](http://xena.ucsc.edu/), you can use `methydifferential_ucsc` to get differential genes. 161 | 162 | ```{r eval=FALSE} 163 | methy_file <- "TCGA.THCA.sampleMap_HumanMethylation450.gz" 164 | methy <- fread(methy_file, sep = "\t", header = T) 165 | library(ChAMP) 166 | myImport <- champ.import(directory=system.file("extdata",package="ChAMPdata")) 167 | myfilter <- champ.filter(beta=myImport$beta,pd=myImport$pd,detP=myImport$detP,beadcount=myImport$beadcount) 168 | cpg_gene <- hm450.manifest.hg19[, c("probeID", "gene_HGNC")] 169 | ## or use IlluminaHumanMethylation450kanno.ilmn12.hg19 to get annotation data 170 | # library(IlluminaHumanMethylation450kanno.ilmn12.hg19) 171 | # ann <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19) 172 | # class(ann) <- "data.frame" 173 | # cpg_gene <- ann[,c("Name", "UCSC_RefGene_Name", "UCSC_RefGene_Group")] 174 | 175 | methy_df <- differential_methy(methy, cpg_gene, ucscData = TRUE) 176 | ``` 177 | 178 | We provide three models to get methylation difference genes: 179 | 180 | if model = "cpg", step1: calculate difference cpgs; step2: calculate difference genes; 181 | 182 | if model = "gene", step1: calculate the methylation level of genes; step2: calculate difference genes. 183 | 184 | We find that only model = "gene" has no deviation of CpG number. 185 | 186 | 187 | Use `clusterProfiler` to do enrichment analytics: 188 | 189 | ```{r eval=FALSE} 190 | differential_gene$p.adj <- p.adjust(differential_gene$pvalue) 191 | genes <- differential_gene[differential_gene$p.adj < 0.05, "gene"] 192 | library(clusterProfiler) 193 | library(enrichplot) 194 | library(org.Hs.eg.db) 195 | ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "SYMBOL") 196 | dotplot(ego) 197 | ``` 198 | 199 | 200 | 201 | ## Copy number variation data integration and differential gene extraction 202 | 203 | use TCGAbiolinks to download TCGA data(Gene Level Copy Number Scores) 204 | 205 | ```{r eval=FALSE} 206 | library(TCGAbiolinks) 207 | query <- GDCquery(project = "TCGA-LGG", 208 | data.category = "Copy Number Variation", 209 | data.type = "Gene Level Copy Number Scores") 210 | 211 | GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path) 212 | 213 | data <- GDCprepare(query = query, 214 | directory = Your_Path) 215 | ``` 216 | 217 | 218 | 219 | Do difference analysis of gene level copy number variation data using `differential_CNV` 220 | 221 | ```{r eval=FALSE} 222 | class(data) <- "data.frame" 223 | cnvData <- data[, -c(1,2,3)] 224 | rownames(cnvData) <- data[, 1] 225 | sampleGroup = sample(c("A","B"), ncol(cnvData), replace = TRUE) 226 | diffCnv <- differential_CNV(cnvData, sampleGroup) 227 | ``` 228 | 229 | Use `clusterProfiler` to do enrichment analytics: 230 | 231 | ```{r eval=FALSE} 232 | pvalues <- diffCnv$pvalue * sign(diffCnv$odds) 233 | genes <- rownames(diffCnv)[diffCnv$pvalue < 0.05] 234 | library(clusterProfiler) 235 | library(enrichplot) 236 | library(org.Hs.eg.db) 237 | ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL") 238 | dotplot(ego) 239 | ``` 240 | 241 | 242 | 243 | ## Difference analysis of single nucleotide Variation data 244 | 245 | Use TCGAbiolinks to download TCGA data 246 | 247 | ```{r eval=FALSE} 248 | library(TCGAbiolinks) 249 | query <- GDCquery(project = "TCGA-ACC", 250 | data.category = "Simple Nucleotide Variation", 251 | data.type = "Masked Somatic Mutation", 252 | workflow.type = "MuSE Variant Aggregation and Masking") 253 | 254 | GDCdownload(query, method = "api", files.per.chunk = 5, directory = Your_Path) 255 | 256 | data_snp <- GDCprepare(query = query, 257 | directory = Your_Path) 258 | 259 | ``` 260 | 261 | 262 | 263 | Use `differential_SNP_tcga` to do difference analysis 264 | 265 | ```{r eval=FALSE} 266 | samples <- unique(data_snp$Tumor_Sample_Barcode) 267 | sampleType <- sample(c("A","B"), length(samples), replace = TRUE) 268 | names(sampleType) <- samples 269 | pvalue <- differential_SNP_tcga(snpData = data_snp, sampleType = sampleType) 270 | # merge pvalue 271 | 272 | 273 | ``` 274 | 275 | 276 | 277 | Use `clusterProfiler` to do enrichment analysis 278 | 279 | ```{r eval=FALSE} 280 | pvalue2 <- sort(pvalue, decreasing = TRUE) 281 | library(clusterProfiler) 282 | library(enrichplot) 283 | library(org.Hs.eg.db) 284 | gsego <- gseGO(pvalue2, OrgDb = org.Hs.eg.db, keyType = "SYMBOL") 285 | dotplot(gsego) 286 | ``` 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | ## GEO chip data processing 295 | 296 | The function `gene_ave` could average the expression data of different ids for the same gene in the GEO chip data. For example: 297 | 298 | ```{r eval=FALSE} 299 | aa <- c("MARCH1","MARC1","MARCH1","MARCH1","MARCH1") 300 | bb <- c(2.969058399,4.722410064,8.165514853,8.24243893,8.60815086) 301 | cc <- c(3.969058399,5.722410064,7.165514853,6.24243893,7.60815086) 302 | file_gene_ave <- data.frame(aa=aa,bb=bb,cc=cc) 303 | colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") 304 | result <- gene_ave(file_gene_ave, 1) 305 | ``` 306 | 307 | Multiple genes symbols may correspond to a same chip id. The result of function `repAssign` is to assign the expression of this id to each gene, and function `repRemove` deletes the expression. For example: 308 | 309 | ```{r} 310 | aa <- c("MARCH1 /// MMA","MARC1","MARCH2 /// MARCH3","MARCH3 /// MARCH4","MARCH1") 311 | bb <- c("2.969058399","4.722410064","8.165514853","8.24243893","8.60815086") 312 | cc <- c("3.969058399","5.722410064","7.165514853","6.24243893","7.60815086") 313 | input_file <- data.frame(aa=aa,bb=bb,cc=cc) 314 | 315 | repAssign_result <- repAssign(input_file," /// ") 316 | repRemove_result <- repRemove(input_file," /// ") 317 | ``` 318 | 319 | ## Other downstream analyses 320 | 1. Especially, the function id_conversion could convert ENSEMBL gene id to gene Symbol in TCGA. For example: 321 | 322 | ```{r} 323 | data(profile) 324 | result <- id_conversion_TCGA(profile) 325 | ``` 326 | 327 | The parameter profile is a data.frame or matrix of gene expression data in TCGA. 328 | 329 | **Note:** In previous versions(< 1.0.0) the `id_conversion` and `id_conversion` used HGNC data to convert human gene id. In future versions, we will use `clusterProfiler::bitr` for ID conversion. 330 | 331 | ```{r} 332 | library(clusterProfiler) 333 | library(org.Hs.eg.db) 334 | bitr(c("A2ML1", "A2ML1-AS1", "A4GALT", "A12M1", "AAAS"), fromType = "SYMBOL", 335 | toType = "ENSEMBL", OrgDb = org.Hs.eg.db, drop = FALSE) 336 | ``` 337 | 338 | 339 | 340 | 2. The function `countToFpkm` and `countToTpm` could convert count data to FPKM or TPM data. 341 | 342 | ```{r} 343 | data(gene_cov) 344 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 345 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 346 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 347 | result <- countToFpkm(lung_squ_count2, 348 | keyType = "SYMBOL", 349 | gene_cov = gene_cov 350 | ) 351 | result 352 | ``` 353 | 354 | ```{r} 355 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 356 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 357 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 358 | result <- countToTpm(lung_squ_count2, 359 | keyType = "SYMBOL", 360 | gene_cov = gene_cov 361 | ) 362 | result 363 | ``` 364 | 365 | **Note:** Now the combined clinical data can be downloaded directly from [TCGAbiolinks](http://www.bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/clinical.html). 366 | 367 | ```{r eval=FALSE} 368 | library(TCGAbiolinks) 369 | ## get BCR Biotab data 370 | query <- GDCquery(project = "TCGA-ACC", 371 | data.category = "Clinical", 372 | data.type = "Clinical Supplement", 373 | data.format = "BCR Biotab") 374 | GDCdownload(query) 375 | clinical.BCRtab.all <- GDCprepare(query) 376 | names(clinical.BCRtab.all) 377 | 378 | ## get indexed data 379 | clinical <- GDCquery_clinic(project = "TCGA-ACC", type = "clinical") 380 | ``` 381 | -------------------------------------------------------------------------------- /data/GSE66705_sample2.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/GSE66705_sample2.rda -------------------------------------------------------------------------------- /data/geneExpress.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/geneExpress.rda -------------------------------------------------------------------------------- /data/gene_cov.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/gene_cov.rda -------------------------------------------------------------------------------- /data/kegg_liver.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/kegg_liver.rda -------------------------------------------------------------------------------- /data/module.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/module.rda -------------------------------------------------------------------------------- /data/profile.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/profile.rda -------------------------------------------------------------------------------- /data/ventricle.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YuLab-SMU/GeoTcgaData/544df0696c75c5a8bb378a8154d41a5269d7d38e/data/ventricle.rda -------------------------------------------------------------------------------- /docs/news/index.html: -------------------------------------------------------------------------------- 1 | 2 | Changelog • GeoTcgaData 6 | 7 | 8 |
9 |
37 | 38 | 39 | 40 |
41 |
42 |
43 |

Changelog

44 | 45 |
46 | 47 |
48 |

GeoTcgaData 1.1.1.993

49 |
  • fix return value of differential_array (2022_10_8, Sat)
    • 50 |
  • fix gene length bug in countToTpm() and countToFpkm()(2022_9_22, Tue)
    • 51 |
  • fix a bug in id_conversion (2022-8-27, Sat)
    • 52 |
53 |
54 |

GeoTcgaData 1.1.12022-08-12

55 |
  • fix a bug in differential_RNA(useTopconfects = TRUE) (2022-8-12, Fir)
56 |
57 |

GeoTcgaData 1.0.3

58 |
  • add function methydifferential_ucsc and methydifferential_limma(2021-10-24, Sun)
59 |
60 |

GeoTcgaData 1.0.22021-10-24

61 |
  • update hgnc_file data(2021-10-24, Sun)
62 |
63 |

GeoTcgaData 1.0.0.12021-07-26

64 |
  • add function differential_RNA to do difference analysis of RNA-seq data(2021-7-20, Tue)
65 |
66 |

GeoTcgaData 0.2.1

67 |
  • add data hgnc_file
    • 68 |
  • update function: id_ava()
    • 69 |
70 |
71 |

GeoTcgaData 0.2.02019-09-23

72 |
  • add functions: ann_merge(), countToFpkm(), countToTpm()
73 |
74 | 75 | 78 | 79 |
80 | 81 | 82 |
91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | -------------------------------------------------------------------------------- /docs/reference/index.html: -------------------------------------------------------------------------------- 1 | 2 | Function reference • GeoTcgaData 6 | 7 | 8 |
9 |
37 | 38 | 39 | 40 |
41 |
42 |
43 |

Reference

44 |
45 | 46 | 50 | 53 | 54 | 57 | 58 | 61 | 62 | 65 | 66 | 69 | 70 | 73 | 74 | 77 | 78 | 81 | 82 | 85 | 86 | 89 | 90 | 93 | 94 | 97 | 98 | 101 | 102 | 105 | 106 | 109 | 110 | 113 | 114 | 117 | 118 | 121 | 122 | 125 | 126 | 129 | 130 | 133 | 134 | 137 | 138 | 141 | 142 | 145 | 146 | 149 | 150 | 153 | 154 | 157 | 158 | 161 | 162 | 165 | 166 | 169 | 170 |
47 |

All functions

48 |

49 |
51 |

differential_array()

52 |

differential_array

55 |

cal_mean_module()

56 |

Find the mean value of the gene in each module

59 |

countToFpkm()

60 |

Convert count to FPKM

63 |

countToTpm()

64 |

Convert count to Tpm

67 |

differential_cnv()

68 |

Do chi-square test to find differential genes

71 |

differential_CNV()

72 |

Do difference analysis of gene level copy number variation data

75 |

differential_limma()

76 |

differential_limma

79 |

differential_RNA()

80 |

Do difference analysis of RNA-seq data

83 |

differential_RNA_ucsc()

84 |

Do difference analysis of RNA-seq data downloaded from ucsc

87 |

differential_SNP()

88 |

Do difference analysis of SNP data

91 |

differential_SNP_tcga()

92 |

Do difference analysis of SNP data downloaded from TCGAbiolinks

95 |

fpkmToTpm_matrix()

96 |

Convert fpkm to Tpm

99 |

geneExpress

100 |

a data.frame of gene expression data

103 |

gene_ave()

104 |

Average the values of same genes in gene expression profile

107 |

gene_cov

108 |

a data.frame of gene length and GC content

111 |

GSE66705_sample2

112 |

a matrix of gene expression data in GEO

115 |

id_ava()

116 |

Gene id conversion types

119 |

id_conversion()

120 |

Convert ENSEMBL gene id to gene Symbol in TCGA

123 |

id_conversion()

124 |

Gene id conversion

127 |

kegg_liver

128 |

a matrix of gene expression data in TCGA

131 |

Merge_methy_tcga()

132 |

Merge methylation data downloaded from TCGA

135 |

methyDiff()

136 |

Get methylation difference gene

139 |

methydifferential_ucsc()

140 |

Title

143 |

module

144 |

a matrix of module name, gene symbols, and the number of gene symbols

147 |

prepare_chi()

148 |

Preparer file for chi-square test

151 |

profile

152 |

a matrix of gene expression data in TCGA

155 |

repAssign()

156 |

Handle the case where one id corresponds to multiple genes

159 |

repRemove()

160 |

Handle the case where one id corresponds to multiple genes

163 |

tcga_cli_deal()

164 |

Combine clinical information obtained from TCGA and extract survival data

167 |

ventricle

168 |

a matrix of gene expression data in GEO

171 | 172 | 175 |
176 | 177 | 178 |
187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | -------------------------------------------------------------------------------- /docs/sitemap.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | /404.html 5 | 6 | 7 | /articles/GeoTcgaData.html 8 | 9 | 10 | /articles/index.html 11 | 12 | 13 | /authors.html 14 | 15 | 16 | /CONDUCT.html 17 | 18 | 19 | /CONTRIBUTING.html 20 | 21 | 22 | /index.html 23 | 24 | 25 | /LICENSE-text.html 26 | 27 | 28 | /news/index.html 29 | 30 | 31 | /reference/differential_array.html 32 | 33 | 34 | /reference/cal_mean_module.html 35 | 36 | 37 | /reference/classify_sample.html 38 | 39 | 40 | /reference/countToFpkm.html 41 | 42 | 43 | /reference/countToTpm.html 44 | 45 | 46 | /reference/differential_cnv.html 47 | 48 | 49 | /reference/differential_CNV.html 50 | 51 | 52 | /reference/differential_gene.html 53 | 54 | 55 | /reference/differential_limma.html 56 | 57 | 58 | /reference/differential_RNA.html 59 | 60 | 61 | /reference/differential_RNA_ucsc.html 62 | 63 | 64 | /reference/differential_SNP.html 65 | 66 | 67 | /reference/differential_SNP_tcga.html 68 | 69 | 70 | /reference/fpkmToTpm_matrix.html 71 | 72 | 73 | /reference/geneExpress.html 74 | 75 | 76 | /reference/gene_ave.html 77 | 78 | 79 | /reference/gene_cov.html 80 | 81 | 82 | /reference/GSE66705_sample2.html 83 | 84 | 85 | /reference/id_ava.html 86 | 87 | 88 | /reference/id_conversion.html 89 | 90 | 91 | /reference/id_conversion.html 92 | 93 | 94 | /reference/index.html 95 | 96 | 97 | /reference/kegg_liver.html 98 | 99 | 100 | /reference/Merge_methy_tcga.html 101 | 102 | 103 | /reference/methyDiff.html 104 | 105 | 106 | /reference/methydifferential_ucsc.html 107 | 108 | 109 | /reference/module.html 110 | 111 | 112 | /reference/prepare_chi.html 113 | 114 | 115 | /reference/profile.html 116 | 117 | 118 | /reference/repAssign.html 119 | 120 | 121 | /reference/repRemove.html 122 | 123 | 124 | /reference/tcga_cli_deal.html 125 | 126 | 127 | /reference/ventricle.html 128 | 129 | 130 | -------------------------------------------------------------------------------- /inst/extdata/build_data.R: -------------------------------------------------------------------------------- 1 | #setwd("E:\\GeoTcgaData_work") 2 | hgnc_file <- data.table::fread("E:\\GeoTcgaData_work\\hgnc_complete_set.txt", 3 | sep = "\t", header = TRUE) 4 | hgnc_file <- dplyr::select(hgnc_file, -c("alias_symbol", "alias_name", 5 | "prev_symbol", "lsdb", "agr")) 6 | class(hgnc_file) <- "data.frame" 7 | gene_loc_len <- GeoTcgaData:::gene_loc_len 8 | hgnc <- GeoTcgaData:::hgnc 9 | genePos <- GeoTcgaData:::genePos 10 | hgnc_file <- GeoTcgaData:::hgnc_file 11 | usethis::use_data(hgnc_file, hgnc, gene_loc_len, 12 | internal = TRUE, compress = "xz", overwrite = TRUE) 13 | 14 | 15 | ## gene_cov 16 | library(TxDb.Hsapiens.UCSC.hg38.knownGene) 17 | library(BSgenome.Hsapiens.UCSC.hg38) 18 | hg38_TxDb <- TxDb.Hsapiens.UCSC.hg38.knownGene 19 | hg38 <- BSgenome.Hsapiens.UCSC.hg38 20 | calc_gene_cov <- function(TxDB, BSGENOME){ 21 | Gene <- genes(TxDB, single.strand.genes.only = FALSE) 22 | Exon <- exons(x = TxDB) 23 | Overlap <- findOverlaps(Exon, Gene) 24 | Exon <- Exon[queryHits(Overlap)] 25 | mcols(Exon)$gene_id <- mcols(Gene[subjectHits(Overlap)])$gene_id 26 | Exon <- split(Exon, mcols(Exon)$gene_id) 27 | Exon <- reduce(Exon) 28 | calculate_cov <- function(x){ 29 | xlen <- sum(width(x)) 30 | xseq <- BSgenome::getSeq(BSGENOME, x) 31 | xGC <- sum(Biostrings::letterFrequency(xseq, 'GC'))/xlen 32 | c(xlen, xGC) 33 | } 34 | gene_cov <- lapply(Exon, calculate_cov) 35 | gene_cov <- gene_cov[names(Gene)] 36 | gene_cov <- t(as.data.frame(gene_cov)) 37 | rownames(gene_cov) <- names(Gene) 38 | colnames(gene_cov) <- c('length', 'GC') 39 | as.data.frame(gene_cov) 40 | } 41 | gene_cov <- calc_gene_cov(TxDB = hg38_TxDb, BSGENOME = hg38) 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /inst/extdata/cnv/00de3/HONGS_p_TCGAb3_75_76_77_NSP_G.txt: -------------------------------------------------------------------------------- 1 | GDC_Aliquot Chromosome Start End Num_Probes Segment_Mean 2 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 1 3301765 55785707 28262 0.0033 3 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 1 55792696 55827469 30 0.5462 4 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 1 55829348 87233893 20119 -0.0012 5 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 1 87237285 87239700 8 -1.1169 6 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 1 87243121 247650984 81057 0.0018 7 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 480597 145900182 76793 0.0034 8 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 145907622 145908689 2 -2.1771 9 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 145908860 193722432 27110 0.0069 10 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 193724639 193733648 7 -1.0391 11 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 193737212 230912395 21841 0.0103 12 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 230912836 230913055 2 -1.5502 13 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 2 230913581 241537572 6229 0.0076 14 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 2170634 170801846 93068 0.0017 15 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 170804047 170833713 18 0.6177 16 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 170834686 181217584 5282 -0.0004 17 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 181220309 181225348 7 -0.8341 18 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 181239123 186849637 2727 0.002 19 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 186850494 186859338 15 0.5654 20 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 3 186860325 197812401 5891 0.0045 21 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 1059384 5815967 2403 0.0006 22 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 5817427 5817557 2 -1.9893 23 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 5817654 29171694 15280 0.001 24 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 29179931 29180476 2 -1.5391 25 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 29180581 52137599 10803 0.0037 26 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 52138506 52139008 2 -1.7386 27 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 52141824 110472535 32956 0.0009 28 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 110475372 110475792 2 -1.7461 29 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 4 110477052 187842528 41865 -0.0004 30 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 5 913983 95230802 50545 0.0016 31 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 5 95236087 95237226 3 -1.9218 32 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 5 95237881 180934240 50395 0.0013 33 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 6 1011760 4574891 2512 -0.0041 34 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 6 4575676 4581489 4 -1.5749 35 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 6 4581494 31204878 16832 0.0056 36 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 6 31208262 31208825 2 1.6481 37 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 6 31210138 170596889 77652 0.0046 38 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 7 664936 40174597 23004 -0.001 39 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 7 40175592 40219954 28 0.5835 40 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 7 40223821 158592540 58784 0.001 41 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 8 667625 144182542 82073 0.0016 42 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 9 789794 138044505 68363 -0.0004 43 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 10 366509 30944103 21620 0.0034 44 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 10 30957229 30978973 27 -0.7166 45 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 10 30979829 133411599 59493 0.0011 46 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 11 456012 26339913 16706 -0.003 47 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 11 26342749 26345172 2 -1.7145 48 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 11 26345232 123812391 53610 0.0006 49 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 11 123812566 123812839 2 -1.7801 50 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 11 123816297 134272740 7131 0.0028 51 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 12 780472 132605822 74211 0.002 52 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 13 18874255 114226675 56874 0.0028 53 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 14 20033191 27398580 3634 0.003 54 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 14 27399274 27507409 51 -0.8739 55 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 14 27507968 38840075 6666 -0.0016 56 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 14 38841818 38863301 12 -1.178 57 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 14 38865374 105533894 39335 0.001 58 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 15 23437561 101344124 44708 0 59 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 16 603333 53175083 18211 0.0032 60 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 16 53177717 53177940 2 -2.6236 61 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 16 53178250 89317317 22941 0.0007 62 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 17 1074619 38769230 14904 -0.0016 63 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 17 38769928 38772885 2 -1.8733 64 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 17 38772947 82959812 22329 0.0021 65 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 18 326691 5947124 2994 -0.0029 66 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 18 5947875 5948061 2 -1.9811 67 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 18 5948488 79349796 39875 0.0014 68 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 19 283868 58370362 24118 0.0013 69 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 20 472817 63588502 37516 -0.0009 70 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 21 13974127 46262057 20561 -0.0025 71 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 22 16934932 31381621 5794 -0.0031 72 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 22 31382595 31384583 2 -1.4397 73 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 22 31388932 48940621 11270 -0.0013 74 | 8dd570dd-2a44-4d3d-bcf2-d9c5c21d00f9 X 3236359 155677414 63397 0.0049 75 | -------------------------------------------------------------------------------- /inst/extdata/cnv/00e8/MICHE_p_TCGAb_428_429_NS.txt: -------------------------------------------------------------------------------- 1 | GDC_Aliquot Chromosome Start End Num_Probes Segment_Mean 2 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 1 3301765 247650984 129760 0.0004 3 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 480597 9808346 5787 0.0102 4 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 9809447 9810242 3 -1.5053 5 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 9813820 70029123 35550 0.0044 6 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 70031855 70032363 3 -0.9245 7 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 70036199 76542146 3636 0.0025 8 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 76552672 76572000 7 -0.9465 9 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 76580922 221887086 75389 0.0053 10 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 221889381 221894838 4 -1.11 11 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 2 221895559 241537572 11830 0.0033 12 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 2170634 10517554 4836 0.0046 13 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 10519258 10520849 2 -1.5264 14 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 10523441 65061928 31955 0.0024 15 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 65061958 65062362 2 -1.3193 16 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 65066087 65446355 334 -0.0061 17 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 65446789 65446808 2 -1.1371 18 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 65448972 80884445 9563 -0.0025 19 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 80886020 80887429 3 -1.267 20 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 3 80890988 197812401 60481 0.0033 21 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 1059384 36534634 21978 0.0017 22 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 36538381 36538411 2 -1.44 23 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 36544419 39205698 1886 -0.0025 24 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 39208967 39210064 2 -1.2461 25 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 39210390 57634986 8283 0.0016 26 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 57635142 57635246 3 -0.9563 27 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 57639758 60483928 1644 0.0017 28 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 60485425 60486464 2 -1.3006 29 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 60486811 111043051 28100 0.001 30 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 111043145 111043496 4 -0.8891 31 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 4 111044539 187842528 41595 0.0019 32 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 913983 17462220 10901 -0.002 33 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 17747964 17805789 62 -1.0687 34 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 17810843 135486593 62140 0.0009 35 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 135489795 135489993 2 -2.9358 36 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 135492523 173001547 23795 0.0018 37 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 173003677 173010638 5 -1.0247 38 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 5 173011703 180934240 4204 0.0039 39 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 6 1011760 145256409 82006 0.0011 40 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 6 145256510 145257320 2 -1.2561 41 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 6 145259422 148392567 2017 0.0148 42 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 6 148393488 148401973 2 -1.4044 43 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 6 148402609 170596889 13141 0.0051 44 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 664936 3762998 1146 -0.0044 45 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 3766579 3766898 2 -1.2036 46 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 3767918 7516194 1521 0.0115 47 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 7518409 7518634 2 -1.2823 48 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 7522447 75626698 35601 0.0044 49 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 75630440 75631353 3 -1.1212 50 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 75631392 102171122 13386 0.0017 51 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 102175051 102175356 2 -1.405 52 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 102176035 126079078 12799 0.0035 53 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 126079396 126079402 2 -1.2042 54 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 7 126081764 158592540 17468 0.0054 55 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 8 667625 144182542 82217 0.0013 56 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 9 789794 138044505 68474 0.0012 57 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 10 366509 17480443 12572 -0.0088 58 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 10 17480684 17480700 2 -1.3838 59 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 10 17481042 133411599 68725 0.0002 60 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 11 456012 134272740 77584 0.0027 61 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 12 780472 132605822 74347 0.0021 62 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 13 18874255 32367536 8277 -0.0004 63 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 13 32367551 32371795 2 -1.3959 64 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 13 32373678 68558328 19791 -0.0026 65 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 13 68565286 68565738 7 -0.6895 66 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 13 68568076 114226675 28917 0.0019 67 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 14 20033191 103895954 49346 -0.0001 68 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 14 103900849 103901391 2 -1.2198 69 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 14 103902012 105533894 433 -0.005 70 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 15 23437561 101344124 44793 0.0021 71 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 16 603333 20586231 9228 0.0011 72 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 16 20595007 20604353 6 0.791 73 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 16 20622023 83760390 29282 0.0026 74 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 16 83760392 83760401 2 -1.6412 75 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 16 83760419 89317317 2698 0.0049 76 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 17 1074619 82959812 37293 0.0006 77 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 18 326691 66976937 36698 0.0002 78 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 18 66978926 67010720 16 0.5568 79 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 18 67014755 79349796 6252 0.0046 80 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 19 283868 58370362 24154 0.0016 81 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 472817 14289450 8860 0.001 82 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 14291481 14296040 2 -1.1918 83 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 14299785 31449137 8201 0.0001 84 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 31452707 31461069 6 -0.6285 85 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 31465102 39405820 4137 0.0048 86 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 39410348 39413403 3 -1.1142 87 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 39417485 46267140 4547 0.0047 88 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 46269102 46269251 2 -1.921 89 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 20 46270491 63588502 11803 0.0052 90 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 21 13974127 24213071 6276 -0.0001 91 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 21 24215936 24216026 3 -1.3743 92 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 21 24216643 46262057 14320 0.0035 93 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f 22 16934932 48940621 17094 0.0001 94 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f X 3236359 13853883 3996 -0.0026 95 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f X 13854003 13854349 3 -3.0405 96 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f X 13856180 79841790 26054 0.01 97 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f X 79842702 79844341 2 -5.3043 98 | 8b50b805-0ecd-46f5-b986-aa9d1ba0831f X 79849126 155677414 33730 0.0124 99 | -------------------------------------------------------------------------------- /inst/extdata/methy/0a0b4/jhu-usc.e.H.4.lvl-3.TCGA-13-1405-01A-01D-0460-05.g.txt: -------------------------------------------------------------------------------- 1 | Composite Element REF Beta_value Chromosome Start End Gene_Symbol Gene_Type Transcript_ID Position_to_TSS CGI_Coordinate Feature_Type 2 | cg00000292 0.869906238572323 chr16 28878779 28878780 ATP2A1;ATP2A1;ATP2A1;ATP2A1;ATP2A1 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000357084.6;ENST00000395503.7;ENST00000536376.4;ENST00000562185.4;ENST00000563975.1 373;290;-1275;-465;-83 CGI:chr16:28879633-28880547 N_Shore 3 | cg00002426 0.116136770722113 chr3 57757816 57757817 SLMAP;SLMAP;SLMAP;SLMAP;SLMAP;SLMAP protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000295951.6;ENST00000295952.6;ENST00000383718.6;ENST00000428312.4;ENST00000449503.5;ENST00000467901.1 1585;368;261;257;257;514 CGI:chr3:57756198-57757263 S_Shore 4 | cg00003994 0.406591482378321 chr7 15686237 15686238 MEOX2 protein_coding ENST00000262041.5 576 CGI:chr7:16399497-16399700 . 5 | cg00005847 0.2148836012527 chr2 176164345 176164346 AC009336.19;HOXD3;HOXD3;HOXD3;RP11-387A1.5 protein_coding;protein_coding;protein_coding;protein_coding;antisense ENST00000468418.4;ENST00000249440.4;ENST00000410016.4;ENST00000432796.2;ENST00000608941.1 13259;267;3453;27387;1372 CGI:chr2:176164685-176165509 N_Shore 6 | cg00006414 0.2148836012527 chr7 149125745 149125746 RN7SL521P;ZNF398;ZNF425;ZNF425 misc_RNA;protein_coding;protein_coding;protein_coding ENST00000488398.3;ENST00000426851.5;ENST00000378061.5;ENST00000483014.1 242;-672;602;562 CGI:chr7:149126122-149127136 N_Shore 7 | cg00007981 0.0111896937323695 chr11 94129428 94129429 PANX1;PANX1 protein_coding;protein_coding ENST00000227638.6;ENST00000436171.2 499;498 CGI:chr11:94128394-94129607 Island 8 | cg00008493 0.994159645759505 chr14 93347431 93347432 COX8C;UNC79 protein_coding;protein_coding ENST00000342144.2;ENST00000256339.7 239;14211 CGI:chr14:93347137-93347765 Island 9 | cg00008713 0.00826646235457973 chr18 11980954 11980955 IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000269159.6;ENST00000383376.8;ENST00000588752.4;ENST00000588927.4;ENST00000589238.4;ENST00000590107.4;ENST00000590138.1;ENST00000625802.1 -475;-563;-72;-717;-84;-512;-658;-475 CGI:chr18:11980484-11982143 Island 10 | cg00009407 0.0108723860129016 chr14 88824577 88824578 TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000338104.9;ENST00000345383.8;ENST00000346301.7;ENST00000354441.9;ENST00000358622.8;ENST00000380656.5;ENST00000536576.4;ENST00000553718.1;ENST00000554686.4;ENST00000555057.4;ENST00000556077.4;ENST00000556567.4;ENST00000556651.4;ENST00000614125.3;ENST00000622513.3 -80;423;-80;-84;423;-86;423;-18;-163;-96;423;-75;-109;423;-48 CGI:chr14:88824574-88825011 Island 11 | cg00010193 0.71090039937439 chr4 1151428 1151429 AC092535.3;TMED11P antisense;unitary_pseudogene ENST00000417557.1;ENST00000479478.4 55;2299 CGI:chr4:1144210-1146033 . 12 | cg00011459 0.90483721870109 chr16 8796568 8796569 PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;TMEM186;TMEM186 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000268261.7;ENST00000562318.4;ENST00000562448.1;ENST00000564030.4;ENST00000564069.1;ENST00000565221.4;ENST00000565896.4;ENST00000566196.4;ENST00000566540.4;ENST00000566604.4;ENST00000566983.4;ENST00000568602.4;ENST00000569958.4;ENST00000570076.4;ENST00000570134.4;ENST00000333050.6;ENST00000564869.1 -1250;-1275;-1275;-1254;-1345;-1300;-1275;-1272;-1275;-1270;7744;-1275;-1300;-1283;-1271;1081;1075 CGI:chr16:8797465-8798071 N_Shore 13 | cg00012199 0.013190421994616 chr14 20682865 20682866 ANG;RNASE4 protein_coding;protein_coding ENST00000336811.9;ENST00000555835.2 -1313;-1236 CGI:chr14:20682759-20683065 Island 14 | cg00012386 0.00562937717912839 chr1 227734811 227734812 JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000366758.6;ENST00000438896.2;ENST00000465251.1;ENST00000480590.1;ENST00000485807.1;ENST00000615711.3;ENST00000620518.3;ENST00000315781.8;ENST00000366759.7;ENST00000366760.4;ENST00000418653.4;ENST00000426344.4;ENST00000470038.1;ENST00000475930.1;ENST00000491439.1;ENST00000617596.3 601;601;-439;266;-433;601;473;-186;-186;6153;-621;-626;-1363;6192;6103;-643 CGI:chr1:227734942-227735730 N_Shore 15 | cg00012792 0.0130914072814042 chr6 8064260 8064261 BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5-TXNDC5;EEF1E1-BLOC1S5 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000244777.5;ENST00000397457.5;ENST00000543936.4;ENST00000627748.1;ENST00000439343.2;ENST00000397456.2 134;155;155;137;105;38271 CGI:chr6:8063813-8064532 Island -------------------------------------------------------------------------------- /inst/extdata/methy/0a0b4/logs/file1.parcel: -------------------------------------------------------------------------------- 1 | cintervaltree.intervaltree 2 | IntervalTree 3 | p0 4 | ((lp1 5 | cintervaltree.interval 6 | Interval 7 | p2 8 | (I0 9 | I1048576 10 | (dp3 11 | S'md5sum' 12 | p4 13 | S'007d09d1333971eb947569f6a4ca3b36' 14 | p5 15 | stp6 16 | Rp7 17 | ag2 18 | (I1048576 19 | I2097152 20 | (dp8 21 | g4 22 | S'9dd013a350555afe41f63985987d2182' 23 | p9 24 | stp10 25 | Rp11 26 | ag2 27 | (I2097152 28 | I3145728 29 | (dp12 30 | g4 31 | S'2d7aa1a88fbb9e6c47364461aac67ad4' 32 | p13 33 | stp14 34 | Rp15 35 | ag2 36 | (I3145728 37 | I4194304 38 | (dp16 39 | g4 40 | S'2ab2df63fdb543e5a103cf3e6701e579' 41 | p17 42 | stp18 43 | Rp19 44 | ag2 45 | (I4194304 46 | I5242880 47 | (dp20 48 | g4 49 | S'267a2c4eb24b1b3ec2776291c75e9cc4' 50 | p21 51 | stp22 52 | Rp23 53 | ag2 54 | (I5242880 55 | I6291456 56 | (dp24 57 | g4 58 | S'cce6ce104a1cedca0335d622c43a4b3b' 59 | p25 60 | stp26 61 | Rp27 62 | ag2 63 | (I6291456 64 | I7340032 65 | (dp28 66 | g4 67 | S'849ececa93632641aca3615754a8bac8' 68 | p29 69 | stp30 70 | Rp31 71 | ag2 72 | (I7340032 73 | I8388608 74 | (dp32 75 | g4 76 | S'9296760f45ccc5105c12a4ca81dc4b0d' 77 | p33 78 | stp34 79 | Rp35 80 | ag2 81 | (I8388608 82 | I9437184 83 | (dp36 84 | g4 85 | S'103e09b9b48151c4a7049220ae2ac54f' 86 | p37 87 | stp38 88 | Rp39 89 | ag2 90 | (I9437184 91 | I9956275 92 | (dp40 93 | g4 94 | S'860d32161e9b8a1cff47d8126124bee0' 95 | p41 96 | stp42 97 | Rp43 98 | atp44 99 | Rp45 100 | . -------------------------------------------------------------------------------- /inst/extdata/methy/0a6b/jhu-usc.e.H.10.lvl-3.TCGA-30-1880-01A-01D-0652-05.g.txt: -------------------------------------------------------------------------------- 1 | Composite Element REF Beta_value Chromosome Start End Gene_Symbol Gene_Type Transcript_ID Position_to_TSS CGI_Coordinate Feature_Type 2 | cg00000292 0.796523939932179 chr16 28878779 28878780 ATP2A1;ATP2A1;ATP2A1;ATP2A1;ATP2A1 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000357084.6;ENST00000395503.7;ENST00000536376.4;ENST00000562185.4;ENST00000563975.1 373;290;-1275;-465;-83 CGI:chr16:28879633-28880547 N_Shore 3 | cg00002426 0.0770646801597857 chr3 57757816 57757817 SLMAP;SLMAP;SLMAP;SLMAP;SLMAP;SLMAP protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000295951.6;ENST00000295952.6;ENST00000383718.6;ENST00000428312.4;ENST00000449503.5;ENST00000467901.1 1585;368;261;257;257;514 CGI:chr3:57756198-57757263 S_Shore 4 | cg00003994 0.0832823123959163 chr7 15686237 15686238 MEOX2 protein_coding ENST00000262041.5 576 CGI:chr7:16399497-16399700 . 5 | cg00005847 0.504193627239696 chr2 176164345 176164346 AC009336.19;HOXD3;HOXD3;HOXD3;RP11-387A1.5 protein_coding;protein_coding;protein_coding;protein_coding;antisense ENST00000468418.4;ENST00000249440.4;ENST00000410016.4;ENST00000432796.2;ENST00000608941.1 13259;267;3453;27387;1372 CGI:chr2:176164685-176165509 N_Shore 6 | cg00006414 0.2148836012527 chr7 149125745 149125746 RN7SL521P;ZNF398;ZNF425;ZNF425 misc_RNA;protein_coding;protein_coding;protein_coding ENST00000488398.3;ENST00000426851.5;ENST00000378061.5;ENST00000483014.1 242;-672;602;562 CGI:chr7:149126122-149127136 N_Shore 7 | cg00007981 0.0228410194656724 chr11 94129428 94129429 PANX1;PANX1 protein_coding;protein_coding ENST00000227638.6;ENST00000436171.2 499;498 CGI:chr11:94128394-94129607 Island 8 | cg00008493 0.98921474501248 chr14 93347431 93347432 COX8C;UNC79 protein_coding;protein_coding ENST00000342144.2;ENST00000256339.7 239;14211 CGI:chr14:93347137-93347765 Island 9 | cg00008713 0.00849766793256932 chr18 11980954 11980955 IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000269159.6;ENST00000383376.8;ENST00000588752.4;ENST00000588927.4;ENST00000589238.4;ENST00000590107.4;ENST00000590138.1;ENST00000625802.1 -475;-563;-72;-717;-84;-512;-658;-475 CGI:chr18:11980484-11982143 Island 10 | cg00009407 0.00600256591763238 chr14 88824577 88824578 TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000338104.9;ENST00000345383.8;ENST00000346301.7;ENST00000354441.9;ENST00000358622.8;ENST00000380656.5;ENST00000536576.4;ENST00000553718.1;ENST00000554686.4;ENST00000555057.4;ENST00000556077.4;ENST00000556567.4;ENST00000556651.4;ENST00000614125.3;ENST00000622513.3 -80;423;-80;-84;423;-86;423;-18;-163;-96;423;-75;-109;423;-48 CGI:chr14:88824574-88825011 Island 11 | cg00010193 0.78874066422712 chr4 1151428 1151429 AC092535.3;TMED11P antisense;unitary_pseudogene ENST00000417557.1;ENST00000479478.4 55;2299 CGI:chr4:1144210-1146033 . 12 | cg00011459 0.950438041209892 chr16 8796568 8796569 PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;TMEM186;TMEM186 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000268261.7;ENST00000562318.4;ENST00000562448.1;ENST00000564030.4;ENST00000564069.1;ENST00000565221.4;ENST00000565896.4;ENST00000566196.4;ENST00000566540.4;ENST00000566604.4;ENST00000566983.4;ENST00000568602.4;ENST00000569958.4;ENST00000570076.4;ENST00000570134.4;ENST00000333050.6;ENST00000564869.1 -1250;-1275;-1275;-1254;-1345;-1300;-1275;-1272;-1275;-1270;7744;-1275;-1300;-1283;-1271;1081;1075 CGI:chr16:8797465-8798071 N_Shore 13 | cg00012199 0.0119642491262264 chr14 20682865 20682866 ANG;RNASE4 protein_coding;protein_coding ENST00000336811.9;ENST00000555835.2 -1313;-1236 CGI:chr14:20682759-20683065 Island 14 | cg00012386 0.00622611896739758 chr1 227734811 227734812 JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000366758.6;ENST00000438896.2;ENST00000465251.1;ENST00000480590.1;ENST00000485807.1;ENST00000615711.3;ENST00000620518.3;ENST00000315781.8;ENST00000366759.7;ENST00000366760.4;ENST00000418653.4;ENST00000426344.4;ENST00000470038.1;ENST00000475930.1;ENST00000491439.1;ENST00000617596.3 601;601;-439;266;-433;601;473;-186;-186;6153;-621;-626;-1363;6192;6103;-643 CGI:chr1:227734942-227735730 N_Shore 15 | cg00012792 0.0125193760585237 chr6 8064260 8064261 BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5-TXNDC5;EEF1E1-BLOC1S5 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000244777.5;ENST00000397457.5;ENST00000543936.4;ENST00000627748.1;ENST00000439343.2;ENST00000397456.2 134;155;155;137;105;38271 CGI:chr6:8063813-8064532 Island -------------------------------------------------------------------------------- /inst/extdata/methy/0a6b/logs/file2.parcel: -------------------------------------------------------------------------------- 1 | cintervaltree.intervaltree 2 | IntervalTree 3 | p0 4 | ((lp1 5 | cintervaltree.interval 6 | Interval 7 | p2 8 | (I0 9 | I1048576 10 | (dp3 11 | S'md5sum' 12 | p4 13 | S'65301bc6a5c8f6838727b70bae72c2b4' 14 | p5 15 | stp6 16 | Rp7 17 | ag2 18 | (I1048576 19 | I2097152 20 | (dp8 21 | g4 22 | S'd1f6b8a428f3244126f82be0a2b0935d' 23 | p9 24 | stp10 25 | Rp11 26 | ag2 27 | (I2097152 28 | I3145728 29 | (dp12 30 | g4 31 | S'42d333e2cced5dc15faba0ca5ef122d5' 32 | p13 33 | stp14 34 | Rp15 35 | ag2 36 | (I3145728 37 | I4194304 38 | (dp16 39 | g4 40 | S'e3551f0661c5c17354a9729d8662383a' 41 | p17 42 | stp18 43 | Rp19 44 | ag2 45 | (I4194304 46 | I5242880 47 | (dp20 48 | g4 49 | S'e38664c435365f5a24408639e7418093' 50 | p21 51 | stp22 52 | Rp23 53 | ag2 54 | (I5242880 55 | I6291456 56 | (dp24 57 | g4 58 | S'250eef3445b553a91902aee338ff4ac4' 59 | p25 60 | stp26 61 | Rp27 62 | ag2 63 | (I6291456 64 | I7340032 65 | (dp28 66 | g4 67 | S'd029fdc9cf26c7f6483bb4d39a36d848' 68 | p29 69 | stp30 70 | Rp31 71 | ag2 72 | (I7340032 73 | I8388608 74 | (dp32 75 | g4 76 | S'f2d9dc657140e23354beeff8a56a9052' 77 | p33 78 | stp34 79 | Rp35 80 | ag2 81 | (I8388608 82 | I9437184 83 | (dp36 84 | g4 85 | S'21352044be69cbd84c4a724957f596d7' 86 | p37 87 | stp38 88 | Rp39 89 | ag2 90 | (I9437184 91 | I9956847 92 | (dp40 93 | g4 94 | S'47b7380df258324a491e9cf284778f98' 95 | p41 96 | stp42 97 | Rp43 98 | atp44 99 | Rp45 100 | . -------------------------------------------------------------------------------- /inst/extdata/methy/0ae7/jhu-usc.H.8.l.I.TCGA-30-1714-01A-02D-0563-05.g.txt: -------------------------------------------------------------------------------- 1 | Composite Element REF Beta_value Chromosome Start End Gene_Symbol Gene_Type Transcript_ID Position_to_TSS CGI_Coordinate Feature_Type 2 | cg00000292 0.771395097495164 chr16 28878779 28878780 ATP2A1;ATP2A1;ATP2A1;ATP2A1;ATP2A1 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000357084.6;ENST00000395503.7;ENST00000536376.4;ENST00000562185.4;ENST00000563975.1 373;290;-1275;-465;-83 CGI:chr16:28879633-28880547 N_Shore 3 | cg00002426 0.0233901750159108 chr3 57757816 57757817 SLMAP;SLMAP;SLMAP;SLMAP;SLMAP;SLMAP protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000295951.6;ENST00000295952.6;ENST00000383718.6;ENST00000428312.4;ENST00000449503.5;ENST00000467901.1 1585;368;261;257;257;514 CGI:chr3:57756198-57757263 S_Shore 4 | cg00003994 0.028704838704695 chr7 15686237 15686238 MEOX2 protein_coding ENST00000262041.5 576 CGI:chr7:16399497-16399700 . 5 | cg00005847 0.903451857318154 chr2 176164345 176164346 AC009336.19;HOXD3;HOXD3;HOXD3;RP11-387A1.5 protein_coding;protein_coding;protein_coding;protein_coding;antisense ENST00000468418.4;ENST00000249440.4;ENST00000410016.4;ENST00000432796.2;ENST00000608941.1 13259;267;3453;27387;1372 CGI:chr2:176164685-176165509 N_Shore 6 | cg00006414 0.2148836012527 chr7 149125745 149125746 RN7SL521P;ZNF398;ZNF425;ZNF425 misc_RNA;protein_coding;protein_coding;protein_coding ENST00000488398.3;ENST00000426851.5;ENST00000378061.5;ENST00000483014.1 242;-672;602;562 CGI:chr7:149126122-149127136 N_Shore 7 | cg00007981 0.00887051985245265 chr11 94129428 94129429 PANX1;PANX1 protein_coding;protein_coding ENST00000227638.6;ENST00000436171.2 499;498 CGI:chr11:94128394-94129607 Island 8 | cg00008493 0.985460674026316 chr14 93347431 93347432 COX8C;UNC79 protein_coding;protein_coding ENST00000342144.2;ENST00000256339.7 239;14211 CGI:chr14:93347137-93347765 Island 9 | cg00008713 0.0102627571258565 chr18 11980954 11980955 IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000269159.6;ENST00000383376.8;ENST00000588752.4;ENST00000588927.4;ENST00000589238.4;ENST00000590107.4;ENST00000590138.1;ENST00000625802.1 -475;-563;-72;-717;-84;-512;-658;-475 CGI:chr18:11980484-11982143 Island 10 | cg00009407 0.00664922618318322 chr14 88824577 88824578 TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000338104.9;ENST00000345383.8;ENST00000346301.7;ENST00000354441.9;ENST00000358622.8;ENST00000380656.5;ENST00000536576.4;ENST00000553718.1;ENST00000554686.4;ENST00000555057.4;ENST00000556077.4;ENST00000556567.4;ENST00000556651.4;ENST00000614125.3;ENST00000622513.3 -80;423;-80;-84;423;-86;423;-18;-163;-96;423;-75;-109;423;-48 CGI:chr14:88824574-88825011 Island 11 | cg00010193 0.624378233369569 chr4 1151428 1151429 AC092535.3;TMED11P antisense;unitary_pseudogene ENST00000417557.1;ENST00000479478.4 55;2299 CGI:chr4:1144210-1146033 . 12 | cg00011459 0.916476198575747 chr16 8796568 8796569 PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;TMEM186;TMEM186 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000268261.7;ENST00000562318.4;ENST00000562448.1;ENST00000564030.4;ENST00000564069.1;ENST00000565221.4;ENST00000565896.4;ENST00000566196.4;ENST00000566540.4;ENST00000566604.4;ENST00000566983.4;ENST00000568602.4;ENST00000569958.4;ENST00000570076.4;ENST00000570134.4;ENST00000333050.6;ENST00000564869.1 -1250;-1275;-1275;-1254;-1345;-1300;-1275;-1272;-1275;-1270;7744;-1275;-1300;-1283;-1271;1081;1075 CGI:chr16:8797465-8798071 N_Shore 13 | cg00012199 0.00897505734686159 chr14 20682865 20682866 ANG;RNASE4 protein_coding;protein_coding ENST00000336811.9;ENST00000555835.2 -1313;-1236 CGI:chr14:20682759-20683065 Island 14 | cg00012386 0.00783581050121143 chr1 227734811 227734812 JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000366758.6;ENST00000438896.2;ENST00000465251.1;ENST00000480590.1;ENST00000485807.1;ENST00000615711.3;ENST00000620518.3;ENST00000315781.8;ENST00000366759.7;ENST00000366760.4;ENST00000418653.4;ENST00000426344.4;ENST00000470038.1;ENST00000475930.1;ENST00000491439.1;ENST00000617596.3 601;601;-439;266;-433;601;473;-186;-186;6153;-621;-626;-1363;6192;6103;-643 CGI:chr1:227734942-227735730 N_Shore 15 | cg00012792 0.00972612443887198 chr6 8064260 8064261 BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5-TXNDC5;EEF1E1-BLOC1S5 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000244777.5;ENST00000397457.5;ENST00000543936.4;ENST00000627748.1;ENST00000439343.2;ENST00000397456.2 134;155;155;137;105;38271 CGI:chr6:8063813-8064532 Island -------------------------------------------------------------------------------- /inst/extdata/methy/0ae7/logs/file3.parcel: -------------------------------------------------------------------------------- 1 | cintervaltree.intervaltree 2 | IntervalTree 3 | p0 4 | ((lp1 5 | cintervaltree.interval 6 | Interval 7 | p2 8 | (I0 9 | I1048576 10 | (dp3 11 | S'md5sum' 12 | p4 13 | S'aead104e6ece37828f89eb77288fe918' 14 | p5 15 | stp6 16 | Rp7 17 | ag2 18 | (I1048576 19 | I2097152 20 | (dp8 21 | g4 22 | S'0773e3100764340b3cc404950897a6fb' 23 | p9 24 | stp10 25 | Rp11 26 | ag2 27 | (I2097152 28 | I3145728 29 | (dp12 30 | g4 31 | S'86ebefee3b2dfc27c2d0b5c9117c971d' 32 | p13 33 | stp14 34 | Rp15 35 | ag2 36 | (I3145728 37 | I4194304 38 | (dp16 39 | g4 40 | S'c05863055e7581a14e6be321d08a2511' 41 | p17 42 | stp18 43 | Rp19 44 | ag2 45 | (I4194304 46 | I5242880 47 | (dp20 48 | g4 49 | S'3fbdb5a920e9f2796cb6dbe7e5485bd7' 50 | p21 51 | stp22 52 | Rp23 53 | ag2 54 | (I5242880 55 | I6291456 56 | (dp24 57 | g4 58 | S'2dfd8445618501c7100903d44dfe0ab5' 59 | p25 60 | stp26 61 | Rp27 62 | ag2 63 | (I6291456 64 | I7340032 65 | (dp28 66 | g4 67 | S'38e743a0a65c8f211f9199cd85ea97db' 68 | p29 69 | stp30 70 | Rp31 71 | ag2 72 | (I7340032 73 | I8388608 74 | (dp32 75 | g4 76 | S'c030bea01e6543263fdf62e8e3504edd' 77 | p33 78 | stp34 79 | Rp35 80 | ag2 81 | (I8388608 82 | I9437184 83 | (dp36 84 | g4 85 | S'a43d34869fe5ba12c29279dd5bb4c9ba' 86 | p37 87 | stp38 88 | Rp39 89 | ag2 90 | (I9437184 91 | I9958646 92 | (dp40 93 | g4 94 | S'1d1cc80e21505dd920dd473d6cda718f' 95 | p41 96 | stp42 97 | Rp43 98 | atp44 99 | Rp45 100 | . -------------------------------------------------------------------------------- /inst/extdata/methy/0b32/jhu-usc.e.H.5.l.TCGA-13-1510-01A-02D-0475-05.gdc_hg38.txt: -------------------------------------------------------------------------------- 1 | Composite Element REF Beta_value Chromosome Start End Gene_Symbol Gene_Type Transcript_ID Position_to_TSS CGI_Coordinate Feature_Type 2 | cg00000292 0.930908590439207 chr16 28878779 28878780 ATP2A1;ATP2A1;ATP2A1;ATP2A1;ATP2A1 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000357084.6;ENST00000395503.7;ENST00000536376.4;ENST00000562185.4;ENST00000563975.1 373;290;-1275;-465;-83 CGI:chr16:28879633-28880547 N_Shore 3 | cg00002426 0.104268079362755 chr3 57757816 57757817 SLMAP;SLMAP;SLMAP;SLMAP;SLMAP;SLMAP protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000295951.6;ENST00000295952.6;ENST00000383718.6;ENST00000428312.4;ENST00000449503.5;ENST00000467901.1 1585;368;261;257;257;514 CGI:chr3:57756198-57757263 S_Shore 4 | cg00003994 0.0401792073366475 chr7 15686237 15686238 MEOX2 protein_coding ENST00000262041.5 576 CGI:chr7:16399497-16399700 . 5 | cg00005847 0.780428197539084 chr2 176164345 176164346 AC009336.19;HOXD3;HOXD3;HOXD3;RP11-387A1.5 protein_coding;protein_coding;protein_coding;protein_coding;antisense ENST00000468418.4;ENST00000249440.4;ENST00000410016.4;ENST00000432796.2;ENST00000608941.1 13259;267;3453;27387;1372 CGI:chr2:176164685-176165509 N_Shore 6 | cg00006414 0.780428197539084 chr7 149125745 149125746 RN7SL521P;ZNF398;ZNF425;ZNF425 misc_RNA;protein_coding;protein_coding;protein_coding ENST00000488398.3;ENST00000426851.5;ENST00000378061.5;ENST00000483014.1 242;-672;602;562 CGI:chr7:149126122-149127136 N_Shore 7 | cg00007981 0.0220541697023661 chr11 94129428 94129429 PANX1;PANX1 protein_coding;protein_coding ENST00000227638.6;ENST00000436171.2 499;498 CGI:chr11:94128394-94129607 Island 8 | cg00008493 0.976977997665785 chr14 93347431 93347432 COX8C;UNC79 protein_coding;protein_coding ENST00000342144.2;ENST00000256339.7 239;14211 CGI:chr14:93347137-93347765 Island 9 | cg00008713 0.0150960841510609 chr18 11980954 11980955 IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2;IMPA2 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000269159.6;ENST00000383376.8;ENST00000588752.4;ENST00000588927.4;ENST00000589238.4;ENST00000590107.4;ENST00000590138.1;ENST00000625802.1 -475;-563;-72;-717;-84;-512;-658;-475 CGI:chr18:11980484-11982143 Island 10 | cg00009407 0.0881331292749216 chr14 88824577 88824578 TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8;TTC8 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000338104.9;ENST00000345383.8;ENST00000346301.7;ENST00000354441.9;ENST00000358622.8;ENST00000380656.5;ENST00000536576.4;ENST00000553718.1;ENST00000554686.4;ENST00000555057.4;ENST00000556077.4;ENST00000556567.4;ENST00000556651.4;ENST00000614125.3;ENST00000622513.3 -80;423;-80;-84;423;-86;423;-18;-163;-96;423;-75;-109;423;-48 CGI:chr14:88824574-88825011 Island 11 | cg00010193 0.630671624077345 chr4 1151428 1151429 AC092535.3;TMED11P antisense;unitary_pseudogene ENST00000417557.1;ENST00000479478.4 55;2299 CGI:chr4:1144210-1146033 . 12 | cg00011459 0.885521863234167 chr16 8796568 8796569 PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;PMM2;TMEM186;TMEM186 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000268261.7;ENST00000562318.4;ENST00000562448.1;ENST00000564030.4;ENST00000564069.1;ENST00000565221.4;ENST00000565896.4;ENST00000566196.4;ENST00000566540.4;ENST00000566604.4;ENST00000566983.4;ENST00000568602.4;ENST00000569958.4;ENST00000570076.4;ENST00000570134.4;ENST00000333050.6;ENST00000564869.1 -1250;-1275;-1275;-1254;-1345;-1300;-1275;-1272;-1275;-1270;7744;-1275;-1300;-1283;-1271;1081;1075 CGI:chr16:8797465-8798071 N_Shore 13 | cg00012199 0.0341206451263948 chr14 20682865 20682866 ANG;RNASE4 protein_coding;protein_coding ENST00000336811.9;ENST00000555835.2 -1313;-1236 CGI:chr14:20682759-20683065 Island 14 | cg00012386 0.0116998743835203 chr1 227734811 227734812 JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;JMJD4;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47;SNAP47 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000366758.6;ENST00000438896.2;ENST00000465251.1;ENST00000480590.1;ENST00000485807.1;ENST00000615711.3;ENST00000620518.3;ENST00000315781.8;ENST00000366759.7;ENST00000366760.4;ENST00000418653.4;ENST00000426344.4;ENST00000470038.1;ENST00000475930.1;ENST00000491439.1;ENST00000617596.3 601;601;-439;266;-433;601;473;-186;-186;6153;-621;-626;-1363;6192;6103;-643 CGI:chr1:227734942-227735730 N_Shore 15 | cg00012792 0.0149194095384671 chr6 8064260 8064261 BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5;BLOC1S5-TXNDC5;EEF1E1-BLOC1S5 protein_coding;protein_coding;protein_coding;protein_coding;protein_coding;protein_coding ENST00000244777.5;ENST00000397457.5;ENST00000543936.4;ENST00000627748.1;ENST00000439343.2;ENST00000397456.2 134;155;155;137;105;38271 CGI:chr6:8063813-8064532 Island -------------------------------------------------------------------------------- /inst/extdata/methy/0b32/logs/file4.parcel: -------------------------------------------------------------------------------- 1 | cintervaltree.intervaltree 2 | IntervalTree 3 | p0 4 | ((lp1 5 | cintervaltree.interval 6 | Interval 7 | p2 8 | (I0 9 | I1048576 10 | (dp3 11 | S'md5sum' 12 | p4 13 | S'efa0b12c00fcd025f9bb24b4bbafdc26' 14 | p5 15 | stp6 16 | Rp7 17 | ag2 18 | (I1048576 19 | I2097152 20 | (dp8 21 | g4 22 | S'86c446ea359acb897b2b622d6d8ff3b8' 23 | p9 24 | stp10 25 | Rp11 26 | ag2 27 | (I2097152 28 | I3145728 29 | (dp12 30 | g4 31 | S'6bae43a0c5c6de9d628114a4987eee28' 32 | p13 33 | stp14 34 | Rp15 35 | ag2 36 | (I3145728 37 | I4194304 38 | (dp16 39 | g4 40 | S'0703a5f69cd342d85bc0dffe4daf0432' 41 | p17 42 | stp18 43 | Rp19 44 | ag2 45 | (I4194304 46 | I5242880 47 | (dp20 48 | g4 49 | S'07b7d7041ec060fdfa4b35fb73b0324b' 50 | p21 51 | stp22 52 | Rp23 53 | ag2 54 | (I5242880 55 | I6291456 56 | (dp24 57 | g4 58 | S'42c174280504a41536414c7c4119ebca' 59 | p25 60 | stp26 61 | Rp27 62 | ag2 63 | (I6291456 64 | I7340032 65 | (dp28 66 | g4 67 | S'4643bfaad70e5fd1e50df13889207d86' 68 | p29 69 | stp30 70 | Rp31 71 | ag2 72 | (I7340032 73 | I8388608 74 | (dp32 75 | g4 76 | S'803aeea62bad0d6a31e63f3e6647e9c4' 77 | p33 78 | stp34 79 | Rp35 80 | ag2 81 | (I8388608 82 | I9437184 83 | (dp36 84 | g4 85 | S'86fe78bd9182ab0920b0e3d2882206f6' 86 | p37 87 | stp38 88 | Rp39 89 | ag2 90 | (I9437184 91 | I9944322 92 | (dp40 93 | g4 94 | S'052e8964fbc18d41c883c15feceae565' 95 | p41 96 | stp42 97 | Rp43 98 | atp44 99 | Rp45 100 | . -------------------------------------------------------------------------------- /man/GSE66705_sample2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{GSE66705_sample2} 5 | \alias{GSE66705_sample2} 6 | \title{a matrix of gene expression data in GEO} 7 | \format{ 8 | A matrix with 999 rows and 3 column 9 | } 10 | \usage{ 11 | GSE66705_sample2 12 | } 13 | \description{ 14 | the first column represents the gene symbol 15 | } 16 | \details{ 17 | the other columns represent the expression of genes 18 | } 19 | \keyword{datasets} 20 | -------------------------------------------------------------------------------- /man/Merge_methy_tcga.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/Merge_methylation.R 3 | \name{Merge_methy_tcga} 4 | \alias{Merge_methy_tcga} 5 | \title{Merge methylation data downloaded from TCGA} 6 | \usage{ 7 | Merge_methy_tcga(dirr = NULL) 8 | } 9 | \arguments{ 10 | \item{dirr}{a string for the directory of methylation data download from tcga 11 | useing the tools gdc} 12 | } 13 | \value{ 14 | a matrix, a combined methylation expression spectrum matrix 15 | } 16 | \description{ 17 | When the methylation data is downloaded from TCGA, 18 | each sample is saved in a folder, which contains the methylation value file 19 | and the descriptive file. This function can directly 20 | extract and consolidate all folders. 21 | } 22 | \examples{ 23 | merge_result <- Merge_methy_tcga(system.file(file.path("extdata", "methy"), 24 | package = "GeoTcgaData")) 25 | } 26 | -------------------------------------------------------------------------------- /man/SNP_QC.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SNP.R 3 | \name{SNP_QC} 4 | \alias{SNP_QC} 5 | \title{Do quality control of SNP data downloaded from TCGAbiolinks} 6 | \usage{ 7 | SNP_QC( 8 | snpData, 9 | geon = 0.02, 10 | mind = 0.02, 11 | maf = 0.05, 12 | hwe = 1e-06, 13 | miss = "NoCall" 14 | ) 15 | } 16 | \arguments{ 17 | \item{snpData}{data.frame of SNP data downloaded from TCGAbiolinks} 18 | 19 | \item{geon}{filters out all variants with missing call rates 20 | exceeding the provided value (default 0.02) to be removed} 21 | 22 | \item{mind}{filters out all samples with missing call rates exceeding 23 | the provided value (default 0.02) to be removed} 24 | 25 | \item{maf}{filters out all variants with minor allele frequency below 26 | the provided threshold} 27 | 28 | \item{hwe}{filters out all variants which have Hardy-Weinberg 29 | equilibrium exact test p-value below the provided threshold} 30 | 31 | \item{miss}{character of miss value} 32 | } 33 | \value{ 34 | data.frame 35 | } 36 | \description{ 37 | Do quality control of SNP data downloaded from TCGAbiolinks 38 | } 39 | \examples{ 40 | # use demo data 41 | snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) 42 | snpDf <- as.data.frame(snpDf) 43 | sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 44 | result <- SNP_QC(snpDf) 45 | } 46 | -------------------------------------------------------------------------------- /man/array_preprocess.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/arrayDiff.R 3 | \name{array_preprocess} 4 | \alias{array_preprocess} 5 | \title{Preprocess of Microarray data} 6 | \usage{ 7 | array_preprocess(x, missing_value = "knn", string = " /// ") 8 | } 9 | \arguments{ 10 | \item{x}{matrix of Microarray data, each column is a sample, 11 | and each row is a gene.} 12 | 13 | \item{missing_value}{Method to impute missing expression data, 14 | one of "zero" and "knn".} 15 | 16 | \item{string}{a string, sep of the gene} 17 | } 18 | \value{ 19 | matrix 20 | } 21 | \description{ 22 | Preprocess of Microarray data 23 | } 24 | \examples{ 25 | \donttest{ 26 | arraylist <- get_geo_array("GSE781") 27 | arraylist <- lapply(arraylist, array_preprocess) 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /man/cal_mean_module.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/calculate_mean_module.R 3 | \name{cal_mean_module} 4 | \alias{cal_mean_module} 5 | \title{Find the mean value of the gene in each module} 6 | \usage{ 7 | cal_mean_module(geneExpress, module) 8 | } 9 | \arguments{ 10 | \item{geneExpress}{a data.frame of gene expression data. 11 | Each column is a sample, and each row is a gene.} 12 | 13 | \item{module}{a data.frame of two column. The first column is module name, 14 | the second column are genes in this module.} 15 | } 16 | \value{ 17 | a data.frame, means the mean of gene expression value in 18 | the same module 19 | } 20 | \description{ 21 | Find the mean value of the gene in each module 22 | } 23 | \examples{ 24 | data(geneExpress) 25 | data(module) 26 | result <- cal_mean_module(geneExpress, module) 27 | } 28 | -------------------------------------------------------------------------------- /man/cluster_array.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/arrayDiff.R 3 | \name{cluster_array} 4 | \alias{cluster_array} 5 | \title{cluster probes of Microarray data} 6 | \usage{ 7 | cluster_array(x, clusterCutoff = 0.7) 8 | } 9 | \arguments{ 10 | \item{x}{matrix of Microarray data, the first is the name of the gene, 11 | and the others are the expression value.} 12 | 13 | \item{clusterCutoff}{Pearson correlation threshold 14 | to cut off the hierarchical tree.} 15 | } 16 | \value{ 17 | data.frame 18 | } 19 | \description{ 20 | cluster probes of Microarray data 21 | } 22 | \examples{ 23 | \donttest{ 24 | arraylist <- get_geo_array("GSE781") 25 | arraylist <- lapply(arraylist, array_preprocess) 26 | arraylist_cluster <- lapply(arraylist, cluster_array) 27 | } 28 | } 29 | -------------------------------------------------------------------------------- /man/combine_pvalue.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SNP.R 3 | \name{combine_pvalue} 4 | \alias{combine_pvalue} 5 | \title{combine pvalues of SNP difference analysis result} 6 | \usage{ 7 | combine_pvalue(snpResult, snp2gene, combineMethod = min) 8 | } 9 | \arguments{ 10 | \item{snpResult}{data.frame of SNP difference analysis result.} 11 | 12 | \item{snp2gene}{data frame of two column: snp and gene.} 13 | 14 | \item{combineMethod}{Method of combining the 15 | pvalue of multiple snp in a gene.} 16 | } 17 | \value{ 18 | data.frame 19 | } 20 | \description{ 21 | combine pvalues of SNP difference analysis result 22 | } 23 | \examples{ 24 | snpResult <- data.frame(pvalue = runif(100), estimate = runif(100)) 25 | rownames(snpResult) <- paste0("snp", seq_len(100)) 26 | snp2gene <- data.frame(snp = rownames(snpResult), 27 | gene = rep(paste0("gene", seq_len(20)), 5)) 28 | result <- combine_pvalue(snpResult, snp2gene) 29 | } 30 | -------------------------------------------------------------------------------- /man/countToFpkm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fpkm_count_conversion.r 3 | \name{countToFpkm} 4 | \alias{countToFpkm} 5 | \title{Convert count to FPKM} 6 | \usage{ 7 | countToFpkm(counts_matrix, keyType = "SYMBOL", gene_cov) 8 | } 9 | \arguments{ 10 | \item{counts_matrix}{a matrix, colnames of counts_matrix are sample name, 11 | rownames of counts_matrix are gene symbols} 12 | 13 | \item{keyType}{keyType, one of keytypes(org.Hs.eg.db).} 14 | 15 | \item{gene_cov}{data.frame of two column, the first column is gene length, 16 | the second column is gene GC content} 17 | } 18 | \value{ 19 | a matrix 20 | } 21 | \description{ 22 | Convert count to FPKM 23 | } 24 | \examples{ 25 | data(gene_cov) 26 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 27 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 28 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 29 | result <- countToFpkm(lung_squ_count2, 30 | keyType = "SYMBOL", 31 | gene_cov = gene_cov 32 | ) 33 | } 34 | -------------------------------------------------------------------------------- /man/countToTpm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fpkm_count_conversion.r 3 | \name{countToTpm} 4 | \alias{countToTpm} 5 | \title{Convert count to Tpm} 6 | \usage{ 7 | countToTpm(counts_matrix, keyType = "SYMBOL", gene_cov) 8 | } 9 | \arguments{ 10 | \item{counts_matrix}{a matrix, colnames of counts_matrix are sample name, 11 | rownames of counts_matrix are gene symbols} 12 | 13 | \item{keyType}{keyType, one of keytypes(org.Hs.eg.db).} 14 | 15 | \item{gene_cov}{data.frame of two column, the first column is gene length, 16 | the second column is gene GC content} 17 | } 18 | \value{ 19 | a matrix 20 | } 21 | \description{ 22 | Convert count to Tpm 23 | } 24 | \examples{ 25 | data(gene_cov) 26 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 27 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 28 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 29 | result <- countToTpm(lung_squ_count2, 30 | keyType = "SYMBOL", 31 | gene_cov = gene_cov 32 | ) 33 | } 34 | -------------------------------------------------------------------------------- /man/differential_RNA.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/RNA_seq.R 3 | \name{differential_RNA} 4 | \alias{differential_RNA} 5 | \title{differential_RNA} 6 | \usage{ 7 | differential_RNA( 8 | counts, 9 | group, 10 | groupCol, 11 | method = "limma", 12 | geneLength = NULL, 13 | gccontent = NULL, 14 | filter = TRUE, 15 | edgeRNorm = TRUE, 16 | adjust.method = "BH", 17 | useTopconfects = TRUE, 18 | ucscData = FALSE 19 | ) 20 | } 21 | \arguments{ 22 | \item{counts}{a dataframe or numeric matrix of raw counts data, 23 | or SummarizedExperiment object} 24 | 25 | \item{group}{sample groups} 26 | 27 | \item{groupCol}{group column} 28 | 29 | \item{method}{one of "DESeq2", "edgeR" , "limma", "dearseq", 30 | "NOISeq", "Wilcoxon", and "auto".} 31 | 32 | \item{geneLength}{a vector of gene length.} 33 | 34 | \item{gccontent}{a vector of gene GC content.} 35 | 36 | \item{filter}{if TRUE, use filterByExpr to filter genes.} 37 | 38 | \item{edgeRNorm}{if TRUE, use edgeR to do normalization for dearseq method.} 39 | 40 | \item{adjust.method}{character string specifying the method used to 41 | adjust p-values for multiple testing. 42 | See \link{p.adjust} for possible values.} 43 | 44 | \item{useTopconfects}{if TRUE, use topconfects to provide a 45 | more biologically useful ranked gene list.} 46 | 47 | \item{ucscData}{Logical, whether the data comes from UCSC Xena.} 48 | } 49 | \value{ 50 | data.frame 51 | } 52 | \description{ 53 | Do difference analysis of RNA-seq data 54 | } 55 | \examples{ 56 | \donttest{ 57 | library(TCGAbiolinks) 58 | 59 | query <- GDCquery( 60 | project = "TCGA-ACC", 61 | data.category = "Transcriptome Profiling", 62 | data.type = "Gene Expression Quantification", 63 | workflow.type = "STAR - Counts" 64 | ) 65 | 66 | GDCdownload(query, 67 | method = "api", files.per.chunk = 3, 68 | directory = Your_Path 69 | ) 70 | 71 | dataRNA <- GDCprepare( 72 | query = query, directory = Your_Path, 73 | save = TRUE, save.filename = "dataRNA.RData" 74 | ) 75 | ## get raw count matrix 76 | dataPrep <- TCGAanalyze_Preprocessing( 77 | object = dataRNA, 78 | cor.cut = 0.6, 79 | datatype = "STAR - Counts" 80 | ) 81 | 82 | # Use `differential_RNA` to do difference analysis. 83 | # We provide the data of human gene length and GC content in `gene_cov`. 84 | group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE) 85 | library(cqn) # To avoid reporting errors: there is no function "rq" 86 | ## get gene length and GC content 87 | library(org.Hs.eg.db) 88 | genes_bitr <- bitr(rownames(gene_cov), 89 | fromType = "ENTREZID", toType = "ENSEMBL", 90 | OrgDb = org.Hs.eg.db, drop = TRUE 91 | ) 92 | genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ] 93 | gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ] 94 | rownames(gene_cov2) <- genes_bitr$ENSEMBL 95 | genes <- intersect(rownames(dataPrep), rownames(gene_cov2)) 96 | dataPrep <- dataPrep[genes, ] 97 | geneLength <- gene_cov2(genes, "length") 98 | gccontent <- gene_cov2(genes, "GC") 99 | names(geneLength) <- names(gccontent) <- genes 100 | ## Difference analysis 101 | DEGAll <- differential_RNA( 102 | counts = dataPrep, group = group, 103 | geneLength = geneLength, gccontent = gccontent 104 | ) 105 | # Use `clusterProfiler` to do enrichment analytics: 106 | diffGenes <- DEGAll$logFC 107 | names(diffGenes) <- rownames(DEGAll) 108 | diffGenes <- sort(diffGenes, decreasing = TRUE) 109 | library(clusterProfiler) 110 | library(enrichplot) 111 | library(org.Hs.eg.db) 112 | gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL") 113 | dotplot(gsego) 114 | } 115 | # use user-defined data 116 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 117 | df <- as.data.frame(df) 118 | rownames(df) <- paste0("gene", 1:25) 119 | colnames(df) <- paste0("sample", 1:16) 120 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 121 | result <- differential_RNA(counts = df, group = group, 122 | filte = FALSE, method = "Wilcoxon") 123 | # use SummarizedExperiment object input 124 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 125 | rownames(df) <- paste0("gene", 1:25) 126 | colnames(df) <- paste0("sample", 1:16) 127 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 128 | 129 | nrows <- 200; ncols <- 20 130 | counts <- matrix( 131 | runif(nrows * ncols, 1, 1e4), nrows, 132 | dimnames = list(paste0("cg",1:200),paste0("S",1:20)) 133 | ) 134 | 135 | colData <- S4Vectors::DataFrame( 136 | row.names = paste0("sample", 1:16), 137 | group = group 138 | ) 139 | data <- SummarizedExperiment::SummarizedExperiment( 140 | assays=S4Vectors::SimpleList(counts=df), 141 | colData = colData) 142 | 143 | result <- differential_RNA(counts = data, groupCol = "group", 144 | filte = FALSE, method = "Wilcoxon") 145 | } 146 | -------------------------------------------------------------------------------- /man/differential_SNP.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SNP.R 3 | \name{differential_SNP} 4 | \alias{differential_SNP} 5 | \title{Do difference analysis of SNP data} 6 | \usage{ 7 | differential_SNP(snpDf, sampleGroup, combineMethod = min) 8 | } 9 | \arguments{ 10 | \item{snpDf}{data.frame of SNP data, each column is a sample, 11 | and each row is a SNP.} 12 | 13 | \item{sampleGroup}{vector of sample group.} 14 | 15 | \item{combineMethod}{Method of combining the 16 | pvalue of multiple snp in a gene.} 17 | } 18 | \value{ 19 | data.frame 20 | } 21 | \description{ 22 | Do difference analysis of SNP data 23 | } 24 | \examples{ 25 | \donttest{ 26 | library(TCGAbiolinks) 27 | query <- GDCquery( 28 | project = "TCGA-CHOL", 29 | data.category = "Simple Nucleotide Variation", 30 | access = "open", 31 | legacy = FALSE, 32 | data.type = "Masked Somatic Mutation", 33 | workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking" 34 | ) 35 | GDCdownload(query) 36 | data_snp <- GDCprepare(query) 37 | samples <- unique(data_snp$Tumor_Sample_Barcode) 38 | sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE) 39 | names(sampleGroup) <- samples 40 | pvalue <- differential_SNP_tcga(snpData = data_snp, 41 | sampleGroup = sampleGroup) 42 | } 43 | # use demo data 44 | snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) 45 | snpDf <- as.data.frame(snpDf) 46 | sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 47 | result <- differential_SNP(snpDf, sampleGroup) 48 | } 49 | -------------------------------------------------------------------------------- /man/differential_SNP_GEO.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SNP.R 3 | \name{differential_SNP_GEO} 4 | \alias{differential_SNP_GEO} 5 | \title{Do difference analysis of SNP data downloaded from GEO} 6 | \usage{ 7 | differential_SNP_GEO(snpData, sampleGroup, method = "Chisquare") 8 | } 9 | \arguments{ 10 | \item{snpData}{data.frame of SNP data downloaded from GEO} 11 | 12 | \item{sampleGroup}{vector of sample group} 13 | 14 | \item{method}{one of "Chisquare", "fisher", 15 | and "CATT"(Cochran-Armitage trend test)} 16 | } 17 | \value{ 18 | data.frame 19 | } 20 | \description{ 21 | Do difference analysis of SNP data downloaded from GEO 22 | } 23 | \examples{ 24 | \donttest{ 25 | file1 <- read.table("GSE66903_series_matrix.txt.gz", 26 | fill=TRUE, comment.char="!", header = TRUE) 27 | rownames(file1) <- file1[, 1] 28 | snpData <- file1[, -1] 29 | sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE) 30 | names(sampleGroup) <- colnames(snpData) 31 | snpData <- SNP_QC(snpData) 32 | sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE) 33 | result1 <- differential_SNP_GEO(snpData = snpData, 34 | sampleGroup = sampleGroup, method = "Chisquare") 35 | } 36 | # use demo data 37 | snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) 38 | snpDf <- as.data.frame(snpDf) 39 | sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 40 | result <- differential_SNP_GEO(snpDf, sampleGroup, method = "fisher") 41 | } 42 | -------------------------------------------------------------------------------- /man/differential_SNP_tcga.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SNP.R 3 | \name{differential_SNP_tcga} 4 | \alias{differential_SNP_tcga} 5 | \title{Do difference analysis of SNP data downloaded from TCGAbiolinks} 6 | \usage{ 7 | differential_SNP_tcga(snpData, sampleGroup, combineMethod = NULL) 8 | } 9 | \arguments{ 10 | \item{snpData}{data.frame of SNP data downloaded from TCGAbiolinks} 11 | 12 | \item{sampleGroup}{vector of sample group} 13 | 14 | \item{combineMethod}{Method of combining the pvalue of 15 | multiple snp in a gene.} 16 | } 17 | \value{ 18 | data.frame 19 | } 20 | \description{ 21 | Do difference analysis of SNP data downloaded from TCGAbiolinks 22 | } 23 | \examples{ 24 | \donttest{ 25 | library(TCGAbiolinks) 26 | query <- GDCquery( 27 | project = "TCGA-CHOL", 28 | data.category = "Simple Nucleotide Variation", 29 | access = "open", 30 | legacy = FALSE, 31 | data.type = "Masked Somatic Mutation", 32 | workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking" 33 | ) 34 | GDCdownload(query) 35 | data_snp <- GDCprepare(query) 36 | samples <- unique(data_snp$Tumor_Sample_Barcode) 37 | sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE) 38 | names(sampleGroup) <- samples 39 | pvalue <- differential_SNP_tcga(snpData = data_snp, 40 | sampleGroup = sampleGroup) 41 | } 42 | # use demo data 43 | snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) 44 | snpDf <- as.data.frame(snpDf) 45 | sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 46 | result <- differential_SNP(snpDf, sampleGroup) 47 | } 48 | -------------------------------------------------------------------------------- /man/differential_array.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/arrayDiff.R 3 | \name{differential_array} 4 | \alias{differential_array} 5 | \title{Differential analysis of Microarray data} 6 | \usage{ 7 | differential_array(df, group, method = "limma", adjust.method = "BH") 8 | } 9 | \arguments{ 10 | \item{df}{data.frame of the omic data, each column is a sample, 11 | and each row is a gene.} 12 | 13 | \item{group}{a vector, group of samples.} 14 | 15 | \item{method}{method to do differential analysis, 16 | one of "limma", "ttest", "wilcox".} 17 | 18 | \item{adjust.method}{adjust.method, one of "holm", "hochberg", "hommel", 19 | "bonferroni", "BH", "BY", "fdr", and "none".} 20 | } 21 | \value{ 22 | data.frame 23 | } 24 | \description{ 25 | Differential analysis of Microarray data 26 | } 27 | \examples{ 28 | \donttest{ 29 | library(GeoTcgaData) 30 | library(data.table) 31 | # Use real GEO data as example 32 | arrayData <- read.table("GSE54807_series_matrix.txt.gz", 33 | sep = "\t", header = TRUE, 34 | fill=TRUE, comment.char = "!", check.names=FALSE) 35 | gpl <- fread("GPL6244-17930.txt", sep = "\t", header = TRUE) 36 | gpl <- gpl[, c("ID", "gene_assignment")] 37 | class(gpl) <- "data.frame" 38 | 39 | for (i in seq_len(nrow(gpl))) { 40 | aa <- strsplit(gpl[i, 2], " // ")[[1]][5] 41 | gpl[i, 2] <- as.character(strsplit(aa, " /// ")[[1]][1]) 42 | } 43 | gpl[,1] <- as.character(gpl[,1]) 44 | arrayData[, 1] <- as.character(arrayData[, 1]) 45 | rownames(gpl) <- gpl[, 1] 46 | arrayData[, 1] <- gpl[arrayData[, 1], 2] 47 | 48 | 49 | arrayData <- repRemove(arrayData," /// ") 50 | 51 | # Remove rows that do not correspond to genes 52 | arrayData <- arrayData[!is.na(arrayData[, 1]), ] 53 | arrayData <- arrayData[!arrayData[, 1] == "", ] 54 | arrayData <- arrayData[!arrayData[, 1] == "---", ] 55 | 56 | 57 | arrayData <- arrayData[order(arrayData[, 1]), ] 58 | arrayData <- gene_ave(arrayData, 1) 59 | 60 | keep <- apply(arrayData, 1, function(x) sum(x < 1) < (length(x)/2)) 61 | arrayData <- arrayData[keep, ] 62 | 63 | group <- c(rep("group1", 12), rep("group2", 12)) 64 | result <- differential_array(df = arrayData, group = group) 65 | } 66 | # Use random data as example 67 | arrayData <- matrix(runif(200), 25, 8) 68 | rownames(arrayData) <- paste0("gene", 1:25) 69 | colnames(arrayData) <- paste0("sample", 1:8) 70 | group <- c(rep("group1", 4), rep("group2", 4)) 71 | result <- differential_array(df = arrayData, group = group) 72 | } 73 | -------------------------------------------------------------------------------- /man/differential_cnv.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/CNV.R 3 | \name{differential_CNV} 4 | \alias{differential_CNV} 5 | \title{Do difference analysis of gene level copy number variation data} 6 | \usage{ 7 | differential_CNV( 8 | cnvData, 9 | sampleGroup, 10 | method = "Chisquare", 11 | adjust.method = "BH", 12 | ... 13 | ) 14 | } 15 | \arguments{ 16 | \item{cnvData}{data.frame of CNV data, each column is a sample, 17 | and each row is a CNV.} 18 | 19 | \item{sampleGroup}{vector of sample group} 20 | 21 | \item{method}{method to do diffenenital analysis, 22 | one of "Chisquare", "fisher", 23 | and "CATT"(Cochran-Armitage trend test)} 24 | 25 | \item{adjust.method}{adjust.method, one of "holm", "hochberg", "hommel", 26 | "bonferroni", "BH", "BY", "fdr", and "none".} 27 | 28 | \item{...}{parameters for "Chisquare", "fisher", 29 | and "CATT"(Cochran-Armitage trend test)} 30 | } 31 | \value{ 32 | data.frame with pvalue and estimate 33 | } 34 | \description{ 35 | Do difference analysis of gene level copy number variation data 36 | } 37 | \examples{ 38 | \donttest{ 39 | # use TCGAbiolinks data as example 40 | library(TCGAbiolinks) 41 | query <- GDCquery( 42 | project = "TCGA-ACC", 43 | data.category = "Copy Number Variation", 44 | data.type = "Gene Level Copy Number", 45 | access = "open" 46 | ) 47 | GDCdownload(query) 48 | cnvData <- GDCprepare(query) 49 | aa <- assays(cnvData)$copy_number 50 | bb <- aa 51 | aa[bb == 2] <- 0 52 | aa[bb < 2] <- -1 53 | aa[bb > 2] <- 1 54 | sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE) 55 | diffCnv <- differential_CNV(aa, sampleGroup) 56 | 57 | # Use sangerbox CNV data as example 58 | cnvData <- fread("Merge_GeneLevelCopyNumber.txt") 59 | class(cnvData) <- "data.frame" 60 | rownames(cnvData) <- cnvData[, 1] 61 | cnvData <- cnvData[, -c(1, 2, 3)] 62 | sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE) 63 | diffCnv <- differential_CNV(cnvData, sampleGroup) 64 | } 65 | # use random data as example 66 | aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) 67 | rownames(aa) <- paste0("gene", 1:25) 68 | colnames(aa) <- paste0("sample", 1:8) 69 | sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) 70 | diffCnv <- differential_CNV(aa, sampleGroup) 71 | } 72 | -------------------------------------------------------------------------------- /man/differential_limma.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/Merge_methylation.R 3 | \name{differential_limma} 4 | \alias{differential_limma} 5 | \title{differential_limma} 6 | \usage{ 7 | differential_limma(df, group, adjust.method = "BH") 8 | } 9 | \arguments{ 10 | \item{df}{data.frame of the omic data} 11 | 12 | \item{group}{a vector, group of samples.} 13 | 14 | \item{adjust.method}{adjust.method.} 15 | } 16 | \value{ 17 | data.frame 18 | } 19 | \description{ 20 | differential_limma 21 | } 22 | \examples{ 23 | df <- matrix(runif(200), 25, 8) 24 | df <- as.data.frame(df) 25 | rownames(df) <- paste0("gene", 1:25) 26 | colnames(df) <- paste0("sample", 1:8) 27 | group <- sample(c("group1", "group2"), 8, replace = TRUE) 28 | result <- differential_limma(df = df, group = group) 29 | } 30 | -------------------------------------------------------------------------------- /man/differential_methy.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/Merge_methylation.R 3 | \name{differential_methy} 4 | \alias{differential_methy} 5 | \title{differential_methy} 6 | \usage{ 7 | differential_methy( 8 | cpgData, 9 | sampleGroup, 10 | groupCol, 11 | combineMethod = "stouffer", 12 | missing_value = "knn", 13 | cpg2gene = NULL, 14 | normMethod = "PBC", 15 | region = "TSS1500", 16 | model = "gene", 17 | adjust.method = "BH", 18 | adjPvalCutoff = 0.05, 19 | ucscData = FALSE 20 | ) 21 | } 22 | \arguments{ 23 | \item{cpgData}{data.frame of cpg beta value, , or SummarizedExperiment object} 24 | 25 | \item{sampleGroup}{vector of sample group} 26 | 27 | \item{groupCol}{group column} 28 | 29 | \item{combineMethod}{method to combine the cpg pvalues, 30 | a function or one of "stouffer", "fisher" and "rhoScores".} 31 | 32 | \item{missing_value}{Method to impute missing expression data, 33 | one of "zero" and "knn".} 34 | 35 | \item{cpg2gene}{data.frame to annotate cpg locus to gene} 36 | 37 | \item{normMethod}{Method to do normalization: "PBC" or "BMIQ".} 38 | 39 | \item{region}{region of genes, one of "Body", "TSS1500", "TSS200", 40 | "3'UTR", "1stExon", "5'UTR", and "IGR". Only used when cpg2gene is NULL.} 41 | 42 | \item{model}{if "cpg", step1: calculate difference cpgs; 43 | step2: calculate difference genes. 44 | if "gene", step1: calculate the methylation level of genes; 45 | step2: calculate difference genes.} 46 | 47 | \item{adjust.method}{character string specifying the method 48 | used to adjust p-values for multiple testing. 49 | See \link{p.adjust} for possible values.} 50 | 51 | \item{adjPvalCutoff}{adjusted pvalue cutoff} 52 | 53 | \item{ucscData}{Logical, whether the data comes from UCSC Xena.} 54 | } 55 | \value{ 56 | data.frame 57 | } 58 | \description{ 59 | Get methylation difference gene 60 | } 61 | \examples{ 62 | \donttest{ 63 | # use TCGAbiolinks data 64 | library(TCGAbiolinks) 65 | query <- GDCquery(project = "TCGA-ACC", 66 | data.category = "DNA Methylation", 67 | data.type = "Methylation Beta Value", 68 | platform = "Illumina Human Methylation 450") 69 | GDCdownload(query, method = "api", files.per.chunk = 5, 70 | directory = Your_Path) 71 | merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data) 72 | library(ChAMP) # To avoid reporting errors 73 | differential_gene <- differential_methy(cpgData = merge_result, 74 | sampleGroup = sample(c("C","T"), 75 | ncol(merge_result[[1]]), replace = TRUE)) 76 | } 77 | # use user defined data 78 | library(ChAMP) 79 | cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 80 | rownames(cpgData) <- paste0("cpg", seq_len(200)) 81 | colnames(cpgData) <- paste0("sample", seq_len(10)) 82 | sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 83 | names(sampleGroup) <- colnames(cpgData) 84 | cpg2gene <- data.frame(cpg = rownames(cpgData), 85 | gene = rep(paste0("gene", seq_len(20)), 10)) 86 | result <- differential_methy(cpgData, sampleGroup, 87 | cpg2gene = cpg2gene, normMethod = NULL) 88 | # use SummarizedExperiment object input 89 | library(ChAMP) 90 | cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 91 | rownames(cpgData) <- paste0("cpg", seq_len(200)) 92 | colnames(cpgData) <- paste0("sample", seq_len(10)) 93 | sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 94 | names(sampleGroup) <- colnames(cpgData) 95 | cpg2gene <- data.frame(cpg = rownames(cpgData), 96 | gene = rep(paste0("gene", seq_len(20)), 10)) 97 | colData <- S4Vectors::DataFrame( 98 | row.names = colnames(cpgData), 99 | group = sampleGroup 100 | ) 101 | data <- SummarizedExperiment::SummarizedExperiment( 102 | assays=S4Vectors::SimpleList(counts=cpgData), 103 | colData = colData) 104 | result <- differential_methy(cpgData = data, 105 | groupCol = "group", normMethod = NULL, 106 | cpg2gene = cpg2gene) 107 | } 108 | -------------------------------------------------------------------------------- /man/fpkmToTpm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fpkm_count_conversion.r 3 | \name{fpkmToTpm} 4 | \alias{fpkmToTpm} 5 | \title{Convert fpkm to Tpm} 6 | \usage{ 7 | fpkmToTpm(fpkm_matrix) 8 | } 9 | \arguments{ 10 | \item{fpkm_matrix}{a matrix, colnames of fpkm_matrix are sample name, 11 | rownames of fpkm_matrix are genes} 12 | } 13 | \value{ 14 | a matrix 15 | } 16 | \description{ 17 | Convert fpkm to Tpm 18 | } 19 | \examples{ 20 | lung_squ_count2 <- matrix(c(0.11, 0.22, 0.43, 0.14, 0.875, 21 | 0.66, 0.77, 0.18, 0.29), ncol = 3) 22 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 23 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 24 | result <- fpkmToTpm(lung_squ_count2) 25 | } 26 | -------------------------------------------------------------------------------- /man/geneExpress.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{geneExpress} 5 | \alias{geneExpress} 6 | \title{a data.frame of gene expression data} 7 | \format{ 8 | A data.frame with 10779 rows and 2 column 9 | } 10 | \usage{ 11 | geneExpress 12 | } 13 | \description{ 14 | It is a randomly generated expression data 15 | used as an example of functions in this package. 16 | the rowname is gene symbols 17 | the columns are gene expression values 18 | } 19 | \keyword{datasets} 20 | -------------------------------------------------------------------------------- /man/gene_ave.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/calculate_mean_profile.R 3 | \name{gene_ave} 4 | \alias{gene_ave} 5 | \title{Average the values of same genes in gene expression profile} 6 | \usage{ 7 | gene_ave(file_gene_ave, k = 1) 8 | } 9 | \arguments{ 10 | \item{file_gene_ave}{a data.frame of gene expression data, 11 | each column is a sample, and each row is a gene.} 12 | 13 | \item{k}{a number, indicates which is the gene column.} 14 | } 15 | \value{ 16 | a data.frame, the values of same genes in gene expression profile 17 | } 18 | \description{ 19 | Average the values of same genes in gene expression profile 20 | } 21 | \examples{ 22 | aa <- c("MARCH1", "MARC1", "MARCH1", "MARCH1", "MARCH1") 23 | bb <- c(2.969058399, 4.722410064, 8.165514853, 8.24243893, 8.60815086) 24 | cc <- c(3.969058399, 5.722410064, 7.165514853, 6.24243893, 7.60815086) 25 | file_gene_ave <- data.frame(aa = aa, bb = bb, cc = cc) 26 | colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") 27 | 28 | result <- gene_ave(file_gene_ave, 1) 29 | } 30 | -------------------------------------------------------------------------------- /man/gene_cov.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{gene_cov} 5 | \alias{gene_cov} 6 | \title{a data.frame of gene length and GC content} 7 | \format{ 8 | A data.frame with 27341 rows and 2 column 9 | } 10 | \usage{ 11 | gene_cov 12 | } 13 | \description{ 14 | the gene length and GC content data comes from 15 | TxDb.Hsapiens.UCSC.hg38.knownGene and 16 | BSgenome.Hsapiens.UCSC.hg38 17 | } 18 | \keyword{datasets} 19 | -------------------------------------------------------------------------------- /man/get_geo_array.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/arrayDiff.R 3 | \name{get_geo_array} 4 | \alias{get_geo_array} 5 | \title{Get Microarray matrix data from GEO} 6 | \usage{ 7 | get_geo_array(gse) 8 | } 9 | \arguments{ 10 | \item{gse}{GSE number, such as GSE781.} 11 | } 12 | \value{ 13 | a list of matrix 14 | } 15 | \description{ 16 | Get Microarray matrix data from GEO 17 | } 18 | \examples{ 19 | \donttest{ 20 | arraylist <- get_geo_array("GSE781") 21 | } 22 | } 23 | -------------------------------------------------------------------------------- /man/id_conversion_TCGA.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/TCGA_id_conversion.R 3 | \name{id_conversion_TCGA} 4 | \alias{id_conversion_TCGA} 5 | \title{Convert ENSEMBL gene id to gene Symbol in TCGA} 6 | \usage{ 7 | id_conversion_TCGA(profiles, toType = "SYMBOL") 8 | } 9 | \arguments{ 10 | \item{profiles}{a data.frame of gene expression data, 11 | each column is a sample, 12 | and each row is a gene.} 13 | 14 | \item{toType}{one of 'keytypes(org.Hs.eg.db)'} 15 | } 16 | \value{ 17 | a data.frame, gene symbols and their expression value 18 | } 19 | \description{ 20 | Convert ENSEMBL gene id to gene Symbol in TCGA 21 | } 22 | \examples{ 23 | library(org.Hs.eg.db) 24 | data(profile) 25 | result <- id_conversion_TCGA(profile) 26 | } 27 | -------------------------------------------------------------------------------- /man/kegg_liver.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{kegg_liver} 5 | \alias{kegg_liver} 6 | \title{a matrix of gene expression data in TCGA} 7 | \format{ 8 | A matrix with 100 rows and 150 column 9 | } 10 | \usage{ 11 | kegg_liver 12 | } 13 | \description{ 14 | It is a randomly generated expression data 15 | used as an example of functions in this package. 16 | the first column represents the gene symbol 17 | } 18 | \details{ 19 | the other columns represent the expression(count) of genes 20 | } 21 | \keyword{datasets} 22 | -------------------------------------------------------------------------------- /man/module.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{module} 5 | \alias{module} 6 | \title{a matrix of module name, gene symbols, and the number of gene symbols} 7 | \format{ 8 | A matrix with 176 rows and 3 column 9 | } 10 | \usage{ 11 | module 12 | } 13 | \description{ 14 | It is a randomly generated expression data 15 | used as an example of functions in this package. 16 | } 17 | \keyword{datasets} 18 | -------------------------------------------------------------------------------- /man/prepare_chi.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/prepareChi.r 3 | \name{prepare_chi} 4 | \alias{prepare_chi} 5 | \title{Preparer file for chi-square test} 6 | \usage{ 7 | prepare_chi(cnv) 8 | } 9 | \arguments{ 10 | \item{cnv}{result of ann_merge()} 11 | } 12 | \value{ 13 | a matrix 14 | } 15 | \description{ 16 | Preparer file for chi-square test 17 | } 18 | \examples{ 19 | cnv <- matrix(c( 20 | -1.09150, -1.47120, -0.87050, -0.50880, 21 | -0.50880, 2.0, 2.0, 2.0, 2.0, 2.0, 2.601962, 2.621332, 2.621332, 22 | 2.621332, 2.621332, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 23 | 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0 24 | ), nrow = 5) 25 | cnv <- as.data.frame(cnv) 26 | rownames(cnv) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3") 27 | colnames(cnv) <- c( 28 | "TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01", 29 | "TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01", 30 | "TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01" 31 | ) 32 | cnv_chi_file <- prepare_chi(cnv) 33 | } 34 | -------------------------------------------------------------------------------- /man/profile.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{profile} 5 | \alias{profile} 6 | \title{a matrix of gene expression data in TCGA} 7 | \format{ 8 | A matrix with 10 rows and 10 column 9 | } 10 | \usage{ 11 | profile 12 | } 13 | \description{ 14 | It is a randomly generated expression data 15 | used as an example of functions in this package. 16 | the first column represents the gene symbol 17 | } 18 | \details{ 19 | the other columns represent the expression(FPKM) of genes 20 | } 21 | \keyword{datasets} 22 | -------------------------------------------------------------------------------- /man/repAssign.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/rep.R 3 | \name{repAssign} 4 | \alias{repAssign} 5 | \title{Handle the case where one id corresponds to multiple genes} 6 | \usage{ 7 | repAssign(input_file, string) 8 | } 9 | \arguments{ 10 | \item{input_file}{input file, a data.frame or a matrix, 11 | the first column should be genes.} 12 | 13 | \item{string}{a string, sep of the gene} 14 | } 15 | \value{ 16 | a data.frame, when an id corresponds to multiple genes, 17 | the expression value is assigned to each gene 18 | } 19 | \description{ 20 | Handle the case where one id corresponds to multiple genes 21 | } 22 | \examples{ 23 | aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 24 | "MARCH3 /// MARCH4", "MARCH1") 25 | bb <- c("2.969058399", "4.722410064", "8.165514853", 26 | "8.24243893", "8.60815086") 27 | cc <- c("3.969058399", "5.722410064", "7.165514853", 28 | "6.24243893", "7.60815086") 29 | input_file <- data.frame(aa = aa, bb = bb, cc = cc) 30 | 31 | repAssign_result <- repAssign(input_file, " /// ") 32 | 33 | } 34 | -------------------------------------------------------------------------------- /man/repRemove.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/rep.R 3 | \name{repRemove} 4 | \alias{repRemove} 5 | \title{Handle the case where one id corresponds to multiple genes} 6 | \usage{ 7 | repRemove(input_file, string) 8 | } 9 | \arguments{ 10 | \item{input_file}{input file, a data.frame or a matrix, 11 | the first column should be genes.} 12 | 13 | \item{string}{a string,sep of the gene} 14 | } 15 | \value{ 16 | a data.frame, when an id corresponds to multiple genes, 17 | the expression value is deleted 18 | } 19 | \description{ 20 | Handle the case where one id corresponds to multiple genes 21 | } 22 | \examples{ 23 | aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 24 | "MARCH3 /// MARCH4", "MARCH1") 25 | bb <- c("2.969058399", "4.722410064", "8.165514853", 26 | "8.24243893", "8.60815086") 27 | cc <- c("3.969058399", "5.722410064", "7.165514853", 28 | "6.24243893", "7.60815086") 29 | input_file <- data.frame(aa = aa, bb = bb, cc = cc) 30 | repRemove_result <- repRemove(input_file, " /// ") 31 | } 32 | -------------------------------------------------------------------------------- /man/ventricle.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{ventricle} 5 | \alias{ventricle} 6 | \title{a matrix of gene expression data in GEO} 7 | \format{ 8 | A matrix with 32 rows and 20 column 9 | } 10 | \usage{ 11 | ventricle 12 | } 13 | \description{ 14 | It is a randomly generated expression data 15 | used as an example of functions in this package. 16 | the first column represents the gene symbol 17 | } 18 | \details{ 19 | the other columns represent the expression of genes 20 | } 21 | \keyword{datasets} 22 | -------------------------------------------------------------------------------- /tests/testthat.R: -------------------------------------------------------------------------------- 1 | # This file is part of the standard setup for testthat. 2 | # It is recommended that you do not modify it. 3 | # 4 | # Where should you do additional test configuration? 5 | # Learn more about the roles of various files in: 6 | # * https://r-pkgs.org/tests.html 7 | # * https://testthat.r-lib.org/reference/test_package.html#special-files 8 | 9 | library(testthat) 10 | library(GeoTcgaData) 11 | 12 | test_check("GeoTcgaData") 13 | -------------------------------------------------------------------------------- /tests/testthat/fpkmToTpm.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example fpkmToTpm_matrix", { 2 | lung_squ_count2 <- matrix(c(0.11, 0.22, 0.43, 0.14, 0.875, 3 | 0.66, 0.77, 0.18, 0.29), ncol = 3) 4 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 5 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 6 | result <- fpkmToTpm(lung_squ_count2) 7 | expect_equal(dim(lung_squ_count2), dim(result)) 8 | }) 9 | -------------------------------------------------------------------------------- /tests/testthat/test_Merge_methy_tcga.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example Merge_methy_tcga", { 2 | merge_result <- Merge_methy_tcga(system.file(file.path("extdata", "methy"), 3 | package = "GeoTcgaData")) 4 | expect_equal(names(merge_result), c("methyResult", "cpg_info")) 5 | }) -------------------------------------------------------------------------------- /tests/testthat/test_arrayDiff.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example differential_array", { 2 | # skip_on_bioc() 3 | arrayData <- matrix(runif(200), 25, 8) 4 | rownames(arrayData) <- paste0("gene", 1:25) 5 | colnames(arrayData) <- paste0("sample", 1:8) 6 | group <- c(rep("group1", 4), rep("group2", 4)) 7 | result <- differential_array(df = arrayData, group = group) 8 | expect_true( "P.Value" %in% colnames(result)) 9 | }) 10 | -------------------------------------------------------------------------------- /tests/testthat/test_cal_mean_module.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example cal_mean_module", { 2 | data(geneExpress) 3 | data(module) 4 | result <- cal_mean_module(geneExpress, module) 5 | expect_equal( ncol(result) , ncol(geneExpress)) 6 | }) -------------------------------------------------------------------------------- /tests/testthat/test_countToFpkm.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example countToFpkm_matrix", { 2 | data(gene_cov) 3 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 4 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 5 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 6 | result <- countToFpkm(lung_squ_count2, 7 | keyType = "SYMBOL", 8 | gene_cov = gene_cov 9 | ) 10 | expect_equal(dim(lung_squ_count2), dim(result)) 11 | }) 12 | -------------------------------------------------------------------------------- /tests/testthat/test_countToTpm.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example countToTpm_matrix", { 2 | data(gene_cov) 3 | lung_squ_count2 <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), ncol = 3) 4 | rownames(lung_squ_count2) <- c("DISC1", "TCOF1", "SPPL3") 5 | colnames(lung_squ_count2) <- c("sample1", "sample2", "sample3") 6 | result <- countToTpm(lung_squ_count2, 7 | keyType = "SYMBOL", 8 | gene_cov = gene_cov 9 | ) 10 | expect_equal(dim(lung_squ_count2), dim(result)) 11 | }) 12 | -------------------------------------------------------------------------------- /tests/testthat/test_diff_RNA_ucsc.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example diff_RNA_ucsc", { 2 | skip_on_cran() 3 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 4 | df <- as.data.frame(df) 5 | rownames(df) <- paste0("gene", 1:25) 6 | colnames(df) <- paste0("sample", 1:16) 7 | df <- log2(df + 1) 8 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 9 | df <- cbind(rownames(df), df) 10 | result <- differential_RNA(counts = df, group = group, 11 | filte = FALSE, method = "limma", ucscData = TRUE) 12 | expect_true( "P.Value" %in% colnames(result)) 13 | }) 14 | -------------------------------------------------------------------------------- /tests/testthat/test_differential_CNV.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example diff_CNV", { 2 | aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) 3 | rownames(aa) <- paste0("gene", 1:25) 4 | colnames(aa) <- paste0("sample", 1:8) 5 | sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) 6 | diffCnv <- differential_CNV(aa, sampleGroup) 7 | expect_true( "P.Value" %in% colnames(diffCnv)) 8 | }) 9 | -------------------------------------------------------------------------------- /tests/testthat/test_differential_RNA.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example differential_RNA", { 2 | skip_on_cran() 3 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 4 | df <- as.data.frame(df) 5 | rownames(df) <- paste0("gene", 1:25) 6 | colnames(df) <- paste0("sample", 1:16) 7 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 8 | result <- differential_RNA(counts = df, group = group, 9 | filte = FALSE, method = "Wilcoxon") 10 | expect_true( "P.Value" %in% colnames(result)) 11 | }) 12 | -------------------------------------------------------------------------------- /tests/testthat/test_differential_limma.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example Diff_limma", { 2 | df <- matrix(runif(200), 25, 8) 3 | df <- as.data.frame(df) 4 | rownames(df) <- paste0("gene", 1:25) 5 | colnames(df) <- paste0("sample", 1:8) 6 | group <- sample(c("group1", "group2"), 8, replace = TRUE) 7 | result <- differential_limma(df = df, group = group) 8 | expect_true( "P.Value" %in% colnames(result)) 9 | }) 10 | -------------------------------------------------------------------------------- /tests/testthat/test_gene_ave.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example gene_ave", { 2 | aa <- c("MARCH1", "MARC1", "MARCH1", "MARCH1", "MARCH1") 3 | bb <- c(2.969058399, 4.722410064, 8.165514853, 8.24243893, 8.60815086) 4 | cc <- c(3.969058399, 5.722410064, 7.165514853, 6.24243893, 7.60815086) 5 | file_gene_ave <- data.frame(aa = aa, bb = bb, cc = cc) 6 | colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") 7 | 8 | result <- gene_ave(file_gene_ave, 1) 9 | expect_equal(sort(unique(file_gene_ave[, 1])), 10 | sort(unique(rownames(result)))) 11 | }) 12 | -------------------------------------------------------------------------------- /tests/testthat/test_prepare_chi.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example prepare_chi", { 2 | cnv <- matrix(c( 3 | -1.09150, -1.47120, -0.87050, -0.50880, 4 | -0.50880, 2.0, 2.0, 2.0, 2.0, 2.0, 2.601962, 2.621332, 2.621332, 5 | 2.621332, 2.621332, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 6 | 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0 7 | ), nrow = 5) 8 | cnv <- as.data.frame(cnv) 9 | rownames(cnv) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3") 10 | colnames(cnv) <- c( 11 | "TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01", 12 | "TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01", 13 | "TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01" 14 | ) 15 | cnv_chi_file <- prepare_chi(cnv) 16 | expect_true("normalCNV" %in% colnames(cnv_chi_file)) 17 | }) 18 | -------------------------------------------------------------------------------- /tests/testthat/test_repAssign.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example rep1", { 2 | aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 3 | "MARCH3 /// MARCH4", "MARCH1") 4 | bb <- c("2.969058399", "4.722410064", "8.165514853", 5 | "8.24243893", "8.60815086") 6 | cc <- c("3.969058399", "5.722410064", "7.165514853", 7 | "6.24243893", "7.60815086") 8 | input_file <- data.frame(aa = aa, bb = bb, cc = cc) 9 | 10 | repAssign_result <- repAssign(input_file, " /// ") 11 | expect_true(!(" /// " %in% repAssign_result[, 1])) 12 | }) 13 | -------------------------------------------------------------------------------- /tests/testthat/test_repRemove.R: -------------------------------------------------------------------------------- 1 | test_that("can parse example rep1", { 2 | aa <- c("MARCH1 /// MMA", "MARC1", "MARCH2 /// MARCH3", 3 | "MARCH3 /// MARCH4", "MARCH1") 4 | bb <- c("2.969058399", "4.722410064", "8.165514853", 5 | "8.24243893", "8.60815086") 6 | cc <- c("3.969058399", "5.722410064", "7.165514853", 7 | "6.24243893", "7.60815086") 8 | input_file <- data.frame(aa = aa, bb = bb, cc = cc) 9 | repRemove_result <- repRemove(input_file, " /// ") 10 | expect_true(!(" /// " %in% repRemove_result[, 1])) 11 | }) 12 | -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/GeoTcgaData.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "GeoTcgaData" 3 | output: rmarkdown::html_vignette 4 | vignette: > 5 | %\VignetteIndexEntry{GeoTcgaData} 6 | %\VignetteEngine{knitr::rmarkdown} 7 | %\VignetteEncoding{UTF-8} 8 | --- 9 | 10 | ```{r, include = FALSE} 11 | knitr::opts_chunk$set( 12 | collapse = TRUE, 13 | comment = "#>" 14 | ) 15 | ``` 16 | -------- 17 | 18 | ## Authors 19 | Erqiang Hu 20 | 21 | Department of Bioinformatics, School of Basic Medical Sciences, 22 | Southern Medical University. 23 | 24 | 25 | ## Introduction 26 | GEO and TCGA provide us with a wealth of data, such as RNA-seq, DNA Methylation, 27 | single nucleotide Variation and Copy number variation data. 28 | It's easy to download data from TCGA using the  gdc tool or `TCGAbiolinks`, 29 | and some software provides organized TCGA data, such as 30 | [UCSC Xena](http://xena.ucsc.edu/) , UCSCXenaTools, and 31 | [sangerbox](http://vip.sangerbox.com/), but processing these data into a format 32 | suitable for bioinformatics  analysis requires more work. This R package was 33 | developed to handle these data. 34 | 35 | ```{r setup} 36 | library(GeoTcgaData) 37 | ``` 38 | 39 | ## Example 40 | 41 | This is a basic example which shows you how to solve a common problem: 42 | 43 | ### RNA-seq data differential expression analysis 44 | 45 | It is convenient to use TCGAbiolinks  or 46 | [`GDCRNATools`](https://bioconductor.org/packages/GDCRNATools/) to download 47 | and analysis Gene expression data.  `TCGAbiolinks` use `edgeR` package to do 48 | differential expression analysis, while `GDCRNATools` can implement three most 49 | commonly used methods: limma, edgeR , and DESeq2 to identify differentially 50 | expressed  genes (DEGs). 51 | 52 | Alicia Oshlack  et al. claimed that unlike the chip data, 53 | the RNA-seq data had one [bias](https://pubmed.ncbi.nlm.nih.gov/20132535/)[1]: 54 | the larger the transcript length / mean read count , the more likely it was to 55 | be  identified as a differential gene, 56 | while there was no such trend in the 57 | [chip data](https://pubmed.ncbi.nlm.nih.gov/19371405/)[2]. 58 | 59 | 60 |  However, when we use their chip data for difference analysis 61 |  (using the limma package), we find that chip data has the same trend as 62 |  RNA-seq data. And we also found this trend in the difference analysis results 63 |  given by the data 64 |  [authors](https://genome.cshlp.org/content/18/9/1509.long)[3]. 65 | 66 |   67 | 68 |  It is worse noting that only technical replicate data, which has small gene 69 |  dispersions, shows this [bias](https://pubmed.ncbi.nlm.nih.gov/28545404/)[4]. 70 |  This is because in technical replicate RNA-seq data a long gene has more 71 |  reads mapping to it compared to a short gene of similar expression, 72 |  and most of the statistical methods used to detect differential expression 73 |  have stronger detection ability for genes with more reads. However, we have 74 |  not deduced why there is such a bias in the current difference 75 |  analysis algorithms. 76 | 77 | Some software, such as [CQN](http://www.bioconductor.org/packages/cqn/) , 78 | present a 79 | [normalization algorithm](https://pubmed.ncbi.nlm.nih.gov/22285995/) [5] 80 | to correct systematic biases(gene length bias and 81 | [GC-content bias](https://pubmed.ncbi.nlm.nih.gov/22177264/)[6]. 82 | But they did not provide sufficient evidence to prove that the correction is 83 | effective. We use the 84 | [Marioni dataset](https://pubmed.ncbi.nlm.nih.gov/19371405/)[2] to verify the 85 | correction effect of CQN and find that there is still a deviation 86 | after correction: 87 | 88 | 89 | 90 | [GOseq](http://bioconductor.org/packages/goseq/) [1]based on 91 | Wallenius' noncentral hypergeometric distribution can effectively correct the 92 | gene length deviation in enrichment analysis. However, the current RNA-seq data 93 | often have no gene length bias, but only the expression amount(read count) 94 | bias, GOseq may overcorrect these data, correcting originally unbiased data 95 | into reverse bias. 96 | 97 | GOseq also fails to correct for expression bias, therefore, read count bias 98 | correction is still a challenge for us. 99 | 100 | ```{r, message=FALSE, warning=FALSE} 101 | # use user-defined data 102 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 103 | df <- as.data.frame(df) 104 | rownames(df) <- paste0("gene", 1:25) 105 | colnames(df) <- paste0("sample", 1:16) 106 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 107 | result <- differential_RNA(counts = df, group = group, 108 | filte = FALSE, method = "Wilcoxon") 109 | # use SummarizedExperiment object input 110 | df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) 111 | rownames(df) <- paste0("gene", 1:25) 112 | colnames(df) <- paste0("sample", 1:16) 113 | group <- sample(c("group1", "group2"), 16, replace = TRUE) 114 | 115 | nrows <- 200; ncols <- 20 116 | counts <- matrix( 117 | runif(nrows * ncols, 1, 1e4), nrows, 118 | dimnames = list(paste0("cg",1:200),paste0("S",1:20)) 119 | ) 120 | 121 | colData <- S4Vectors::DataFrame( 122 | row.names = paste0("sample", 1:16), 123 | group = group 124 | ) 125 | data <- SummarizedExperiment::SummarizedExperiment( 126 | assays=S4Vectors::SimpleList(counts=df), 127 | colData = colData) 128 | 129 | result <- differential_RNA(counts = data, groupCol = "group", 130 | filte = FALSE, method = "Wilcoxon") 131 | ``` 132 | 133 | 134 | ### DNA Methylation data integration 135 | 136 | use `TCGAbiolinks` data. 137 | 138 | The codes may need to be modified if `TCGAbiolinks` updates. 139 | So please read its documents. 140 | 141 | ```{r, message=FALSE, warning=FALSE} 142 | # use user defined data 143 | library(ChAMP) 144 | cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 145 | rownames(cpgData) <- paste0("cpg", seq_len(200)) 146 | colnames(cpgData) <- paste0("sample", seq_len(10)) 147 | sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 148 | names(sampleGroup) <- colnames(cpgData) 149 | cpg2gene <- data.frame(cpg = rownames(cpgData), 150 | gene = rep(paste0("gene", seq_len(20)), 10)) 151 | result <- differential_methy(cpgData, sampleGroup, 152 | cpg2gene = cpg2gene, normMethod = NULL) 153 | # use SummarizedExperiment object input 154 | library(ChAMP) 155 | cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) 156 | rownames(cpgData) <- paste0("cpg", seq_len(200)) 157 | colnames(cpgData) <- paste0("sample", seq_len(10)) 158 | sampleGroup <- c(rep("group1", 5), rep("group2", 5)) 159 | names(sampleGroup) <- colnames(cpgData) 160 | cpg2gene <- data.frame(cpg = rownames(cpgData), 161 | gene = rep(paste0("gene", seq_len(20)), 10)) 162 | colData <- S4Vectors::DataFrame( 163 | row.names = colnames(cpgData), 164 | group = sampleGroup 165 | ) 166 | data <- SummarizedExperiment::SummarizedExperiment( 167 | assays=S4Vectors::SimpleList(counts=cpgData), 168 | colData = colData) 169 | result <- differential_methy(cpgData = data, 170 | groupCol = "group", normMethod = NULL, 171 | cpg2gene = cpg2gene) 172 | ``` 173 | **Note:** `ChAMP`has a large number of dependent packages. 174 | If you cannot install it successfully, you can download each dependent package 175 | separately(Source or Binary) and install it locally. 176 | 177 | We provide two models to get methylation difference genes: 178 | 179 | if model = "cpg", step1: calculate difference cpgs; 180 | step2: calculate difference genes; 181 | 182 | if model = "gene", step1: calculate the methylation level of genes; 183 | step2: calculate difference genes. 184 | 185 | We find that only model = "gene" has no deviation of CpG number. 186 | 187 | 188 | ### Copy number variation data integration and differential gene extraction 189 | 190 | use TCGAbiolinks to download TCGA data(Gene Level Copy Number Scores) 191 | 192 | ```{r, message=FALSE, warning=FALSE} 193 | # use random data as example 194 | aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) 195 | rownames(aa) <- paste0("gene", 1:25) 196 | colnames(aa) <- paste0("sample", 1:8) 197 | sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) 198 | diffCnv <- differential_CNV(aa, sampleGroup) 199 | ``` 200 | 201 | 202 | 203 | ### Difference analysis of single nucleotide Variation data 204 | We provide SNP_QC function to do quality control of SNP data 205 | ```{r, message=FALSE, warning=FALSE} 206 | snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) 207 | snpDf <- as.data.frame(snpDf) 208 | sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 209 | result <- SNP_QC(snpDf) 210 | ``` 211 | 212 | Then use differential_SNP to do differential analysis. 213 | ```{r, message=FALSE, warning=FALSE} 214 | #' snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) 215 | #' snpDf <- as.data.frame(snpDf) 216 | #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) 217 | #' result <- differential_SNP(snpDf, sampleGroup) 218 | ``` 219 | 220 | 221 | ### GEO chip data processing 222 | The function `gene_ave` could average the expression data of different 223 | ids for the same gene in the GEO chip data. For example: 224 | 225 | ```{r, message=FALSE, warning=FALSE} 226 | aa <- c("MARCH1","MARC1","MARCH1","MARCH1","MARCH1") 227 | bb <- c(2.969058399,4.722410064,8.165514853,8.24243893,8.60815086) 228 | cc <- c(3.969058399,5.722410064,7.165514853,6.24243893,7.60815086) 229 | file_gene_ave <- data.frame(aa=aa,bb=bb,cc=cc) 230 | colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") 231 | result <- gene_ave(file_gene_ave, 1) 232 | ``` 233 | 234 | Multiple genes symbols may correspond to a same chip id. The result of 235 | function `repAssign` is to assign the expression of this id to each gene, 236 | and function `repRemove` deletes the expression. For example: 237 | 238 | ```{r} 239 | aa <- c("MARCH1 /// MMA","MARC1","MARCH2 /// MARCH3", 240 | "MARCH3 /// MARCH4","MARCH1") 241 | bb <- c("2.969058399","4.722410064","8.165514853","8.24243893","8.60815086") 242 | cc <- c("3.969058399","5.722410064","7.165514853","6.24243893","7.60815086") 243 | input_file <- data.frame(aa=aa,bb=bb,cc=cc) 244 | repAssign_result <- repAssign(input_file," /// ") 245 | repRemove_result <- repRemove(input_file," /// ") 246 | ``` 247 | 248 | ### Other downstream analyses 249 | 250 | 1. The function `id_conversion_TCGA` could convert ENSEMBL gene id to 251 | gene Symbol in TCGA. For example: 252 | 253 | ```{r, message=FALSE, warning=FALSE} 254 | data(profile) 255 | result <- id_conversion_TCGA(profile) 256 | ``` 257 | 258 | The parameter `profile` is a data.frame or matrix of gene expression 259 | data in TCGA. 260 | 261 | **Note:** In previous versions(< 1.0.0) the `id_conversion` and 262 | `id_conversion_TCGA` used HGNC data to convert human gene id. 263 | In future versions, we will use `clusterProfiler::bitr` for ID conversion. 264 | 265 | 266 | 2. The function `countToFpkm` and `countToTpm` could convert 267 | count data to FPKM or TPM data. 268 | 269 | ```{r} 270 | data(gene_cov) 271 | lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9),ncol=3) 272 | rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3") 273 | colnames(lung_squ_count2) <- c("sample1","sample2","sample3") 274 | result <- countToFpkm(lung_squ_count2, keyType = "SYMBOL", 275 | gene_cov = gene_cov) 276 | ``` 277 | 278 | ```{r, message=FALSE, warning=FALSE} 279 | data(gene_cov) 280 | lung_squ_count2 <- matrix(c(0.11,0.22,0.43,0.14,0.875, 281 | 0.66,0.77,0.18,0.29),ncol=3) 282 | rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3") 283 | colnames(lung_squ_count2) <- c("sample1","sample2","sample3") 284 | result <- countToTpm(lung_squ_count2, keyType = "SYMBOL", 285 | gene_cov = gene_cov) 286 | ``` 287 | 288 | ```{r} 289 | sessionInfo() 290 | ``` 291 | 292 | ## References 293 | 1. Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis 294 | for RNA-seq: accounting for selection bias. Genome Biol 11: R14. 295 | 2. Oshlack A, Wakefield MJ (2009) Transcript length bias in RNA-seq data 296 | confounds systems biology. Biol Direct 4: 14. 297 | 3. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an 298 | assessment of technical reproducibility and comparison with gene expression 299 | arrays. Genome Res 18: 1509-1517. 300 | 4. Yoon S, Nam D (2017) Gene dispersion is the key determinant of the read 301 | count bias in differential expression analysis of RNA-seq data. 302 | BMC Genomics 18: 408. 303 | 5. Hansen KD, Irizarry RA, Wu Z (2012) Removing technical variability in 304 | RNA-seq data using conditional quantile normalization. 305 | Biostatistics 13: 204-216. 306 | 6. Risso D, Schwartz K, Sherlock G, Dudoit S (2011) GC-content normalization 307 | for RNA-Seq data. BMC Bioinformatics 12: 480. 308 | 309 | --------------------------------------------------------------------------------