## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(GeoTcgaData) ## ----message=FALSE, warning=FALSE--------------------------------------------- # use user-defined data df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) df <- as.data.frame(df) rownames(df) <- paste0("gene", 1:25) colnames(df) <- paste0("sample", 1:16) group <- sample(c("group1", "group2"), 16, replace = TRUE) result <- differential_RNA(counts = df, group = group, filte = FALSE, method = "Wilcoxon") # use SummarizedExperiment object input df <- matrix(rnbinom(400, mu = 4, size = 10), 25, 16) rownames(df) <- paste0("gene", 1:25) colnames(df) <- paste0("sample", 1:16) group <- sample(c("group1", "group2"), 16, replace = TRUE) nrows <- 200; ncols <- 20 counts <- matrix( runif(nrows * ncols, 1, 1e4), nrows, dimnames = list(paste0("cg",1:200),paste0("S",1:20)) ) colData <- S4Vectors::DataFrame( row.names = paste0("sample", 1:16), group = group ) data <- SummarizedExperiment::SummarizedExperiment( assays=S4Vectors::SimpleList(counts=df), colData = colData) result <- differential_RNA(counts = data, groupCol = "group", filte = FALSE, method = "Wilcoxon") ## ----message=FALSE, warning=FALSE--------------------------------------------- # use user defined data library(ChAMP) cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) rownames(cpgData) <- paste0("cpg", seq_len(200)) colnames(cpgData) <- paste0("sample", seq_len(10)) sampleGroup <- c(rep("group1", 5), rep("group2", 5)) names(sampleGroup) <- colnames(cpgData) cpg2gene <- data.frame(cpg = rownames(cpgData), gene = rep(paste0("gene", seq_len(20)), 10)) result <- differential_methy(cpgData, sampleGroup, cpg2gene = cpg2gene, normMethod = NULL) # use SummarizedExperiment object input library(ChAMP) cpgData <- matrix(runif(2000), nrow = 200, ncol = 10) rownames(cpgData) <- paste0("cpg", seq_len(200)) colnames(cpgData) <- paste0("sample", seq_len(10)) sampleGroup <- c(rep("group1", 5), rep("group2", 5)) names(sampleGroup) <- colnames(cpgData) cpg2gene <- data.frame(cpg = rownames(cpgData), gene = rep(paste0("gene", seq_len(20)), 10)) colData <- S4Vectors::DataFrame( row.names = colnames(cpgData), group = sampleGroup ) data <- SummarizedExperiment::SummarizedExperiment( assays=S4Vectors::SimpleList(counts=cpgData), colData = colData) result <- differential_methy(cpgData = data, groupCol = "group", normMethod = NULL, cpg2gene = cpg2gene) ## ----message=FALSE, warning=FALSE--------------------------------------------- # use random data as example aa <- matrix(sample(c(0, 1, -1), 200, replace = TRUE), 25, 8) rownames(aa) <- paste0("gene", 1:25) colnames(aa) <- paste0("sample", 1:8) sampleGroup <- sample(c("A", "B"), ncol(aa), replace = TRUE) diffCnv <- differential_CNV(aa, sampleGroup) ## ----message=FALSE, warning=FALSE--------------------------------------------- snpDf <- matrix(sample(c("AA", "Aa", "aa"), 100, replace = TRUE), 10, 10) snpDf <- as.data.frame(snpDf) sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) result <- SNP_QC(snpDf) ## ----message=FALSE, warning=FALSE--------------------------------------------- #' snpDf <- matrix(sample(c("mutation", NA), 100, replace = TRUE), 10, 10) #' snpDf <- as.data.frame(snpDf) #' sampleGroup <- sample(c("A", "B"), 10, replace = TRUE) #' result <- differential_SNP(snpDf, sampleGroup) ## ----message=FALSE, warning=FALSE--------------------------------------------- aa <- c("MARCH1","MARC1","MARCH1","MARCH1","MARCH1") bb <- c(2.969058399,4.722410064,8.165514853,8.24243893,8.60815086) cc <- c(3.969058399,5.722410064,7.165514853,6.24243893,7.60815086) file_gene_ave <- data.frame(aa=aa,bb=bb,cc=cc) colnames(file_gene_ave) <- c("Gene", "GSM1629982", "GSM1629983") result <- gene_ave(file_gene_ave, 1) ## ----------------------------------------------------------------------------- aa <- c("MARCH1 /// MMA","MARC1","MARCH2 /// MARCH3", "MARCH3 /// MARCH4","MARCH1") bb <- c("2.969058399","4.722410064","8.165514853","8.24243893","8.60815086") cc <- c("3.969058399","5.722410064","7.165514853","6.24243893","7.60815086") input_file <- data.frame(aa=aa,bb=bb,cc=cc) repAssign_result <- repAssign(input_file," /// ") repRemove_result <- repRemove(input_file," /// ") ## ----message=FALSE, warning=FALSE--------------------------------------------- data(profile) result <- id_conversion_TCGA(profile) ## ----------------------------------------------------------------------------- data(gene_cov) lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9),ncol=3) rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3") colnames(lung_squ_count2) <- c("sample1","sample2","sample3") result <- countToFpkm(lung_squ_count2, keyType = "SYMBOL", gene_cov = gene_cov) ## ----message=FALSE, warning=FALSE--------------------------------------------- data(gene_cov) lung_squ_count2 <- matrix(c(0.11,0.22,0.43,0.14,0.875, 0.66,0.77,0.18,0.29),ncol=3) rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3") colnames(lung_squ_count2) <- c("sample1","sample2","sample3") result <- countToTpm(lung_squ_count2, keyType = "SYMBOL", gene_cov = gene_cov) ## ----------------------------------------------------------------------------- sessionInfo()