Package 'GeoTcgaData' reference manual

Title:	Processing Various Types of Data on GEO and TCGA
Description:	Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.
Authors:	Erqiang Hu [aut, cre]
Maintainer:	Erqiang Hu <[email protected]>
License:	Artistic-2.0
Version:	2.7.0
Built:	2024-10-30 07:45:20 UTC
Source:	https://github.com/bioc/GeoTcgaData

Preprocess of Microarray data

Description

Preprocess of Microarray data

Usage

array_preprocess(x, missing_value = "knn", string = " /// ")
array_preprocess(x, missing_value = "knn", string = " /// ")

Arguments

`x`	matrix of Microarray data, each column is a sample, and each row is a gene.
`missing_value`	Method to impute missing expression data, one of "zero" and "knn".
`string`	a string, sep of the gene

Value

matrix

Examples


arraylist <- get_geo_array("GSE781")
arraylist <- lapply(arraylist, array_preprocess)

arraylist <- get_geo_array("GSE781")
arraylist <- lapply(arraylist, array_preprocess)

Find the mean value of the gene in each module

Description

Find the mean value of the gene in each module

Usage

cal_mean_module(geneExpress, module)
cal_mean_module(geneExpress, module)

Arguments

`geneExpress`	a data.frame of gene expression data. Each column is a sample, and each row is a gene.
`module`	a data.frame of two column. The first column is module name, the second column are genes in this module.

Value

a data.frame, means the mean of gene expression value in the same module

Examples

data(geneExpress)
data(module)
result <- cal_mean_module(geneExpress, module)
data(geneExpress)
data(module)
result <- cal_mean_module(geneExpress, module)

cluster probes of Microarray data

Description

cluster probes of Microarray data

Usage

cluster_array(x, clusterCutoff = 0.7)
cluster_array(x, clusterCutoff = 0.7)

Arguments

`x`	matrix of Microarray data, the first is the name of the gene, and the others are the expression value.
`clusterCutoff`	Pearson correlation threshold to cut off the hierarchical tree.

Value

data.frame

Examples


arraylist <- get_geo_array("GSE781")
arraylist <- lapply(arraylist, array_preprocess)
arraylist_cluster <- lapply(arraylist, cluster_array)

arraylist <- get_geo_array("GSE781")
arraylist <- lapply(arraylist, array_preprocess)
arraylist_cluster <- lapply(arraylist, cluster_array)

combine pvalues of SNP difference analysis result

Description

combine pvalues of SNP difference analysis result

Usage

combine_pvalue(snpResult, snp2gene, combineMethod = min)
combine_pvalue(snpResult, snp2gene, combineMethod = min)

Arguments

`snpResult`	data.frame of SNP difference analysis result.
`snp2gene`	data frame of two column: snp and gene.
`combineMethod`	Method of combining the pvalue of multiple snp in a gene.

Value

data.frame

Examples

snpResult <- data.frame(pvalue = runif(100), estimate = runif(100))
rownames(snpResult) <- paste0("snp", seq_len(100))
snp2gene <- data.frame(snp = rownames(snpResult), 
    gene = rep(paste0("gene", seq_len(20)), 5))
result <- combine_pvalue(snpResult, snp2gene)
snpResult <- data.frame(pvalue = runif(100), estimate = runif(100))
rownames(snpResult) <- paste0("snp", seq_len(100))
snp2gene <- data.frame(snp = rownames(snpResult), 
    gene = rep(paste0("gene", seq_len(20)), 5))
result <- combine_pvalue(snpResult, snp2gene)

Convert count to FPKM

Description

Convert count to FPKM

Usage

countToFpkm(counts_matrix, keyType = "SYMBOL", gene_cov)
countToFpkm(counts_matrix, keyType = "SYMBOL", gene_cov)

Arguments

`counts_matrix`	a matrix, colnames of counts_matrix are sample name, rownames of counts_matrix are gene symbols
`keyType`	keyType, one of keytypes(org.Hs.eg.db).
`gene_cov`	data.frame of two column, the first column is gene length, the second column is gene GC content

Value

a matrix

Examples

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
)
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
)

Convert count to Tpm

Description

Convert count to Tpm

Usage

countToTpm(counts_matrix, keyType = "SYMBOL", gene_cov)
countToTpm(counts_matrix, keyType = "SYMBOL", gene_cov)

Arguments

`counts_matrix`	a matrix, colnames of counts_matrix are sample name, rownames of counts_matrix are gene symbols
`keyType`	keyType, one of keytypes(org.Hs.eg.db).
`gene_cov`	data.frame of two column, the first column is gene length, the second column is gene GC content

Value

a matrix

Examples

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 <- countToTpm(lung_squ_count2,
    keyType = "SYMBOL",
    gene_cov = gene_cov
)
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 <- countToTpm(lung_squ_count2,
    keyType = "SYMBOL",
    gene_cov = gene_cov
)

Differential analysis of Microarray data

Description

Differential analysis of Microarray data

Usage

differential_array(df, group, method = "limma", adjust.method = "BH")
differential_array(df, group, method = "limma", adjust.method = "BH")

Arguments

`df`	data.frame of the omic data, each column is a sample, and each row is a gene.
`group`	a vector, group of samples.
`method`	method to do differential analysis, one of "limma", "ttest", "wilcox".
`adjust.method`	adjust.method, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none".

Value

data.frame

Examples


library(GeoTcgaData)
library(data.table)
# Use real GEO data as example
arrayData <- read.table("GSE54807_series_matrix.txt.gz",
    sep = "\t", header = TRUE,
        fill=TRUE, comment.char = "!", check.names=FALSE)
gpl <- fread("GPL6244-17930.txt", sep = "\t", header = TRUE)
gpl <- gpl[, c("ID", "gene_assignment")]
class(gpl) <- "data.frame"

for (i in seq_len(nrow(gpl))) {
        aa <- strsplit(gpl[i, 2], " // ")[[1]][5]
        gpl[i, 2] <- as.character(strsplit(aa, " /// ")[[1]][1])
}
gpl[,1] <- as.character(gpl[,1])
arrayData[, 1] <- as.character(arrayData[, 1])
rownames(gpl) <- gpl[, 1]
arrayData[, 1] <- gpl[arrayData[, 1], 2]


arrayData <- repRemove(arrayData," /// ")

# Remove rows that do not correspond to genes
arrayData <- arrayData[!is.na(arrayData[, 1]), ]
arrayData <- arrayData[!arrayData[, 1] == "", ]
arrayData <- arrayData[!arrayData[, 1] == "---", ]


arrayData <- arrayData[order(arrayData[, 1]), ]
arrayData <- gene_ave(arrayData, 1)

keep <- apply(arrayData, 1, function(x) sum(x < 1) < (length(x)/2))
arrayData <- arrayData[keep, ]

group <- c(rep("group1", 12), rep("group2", 12))
result <- differential_array(df = arrayData, group = group)

# Use random data as example
arrayData <- matrix(runif(200), 25, 8)
rownames(arrayData) <- paste0("gene", 1:25)
colnames(arrayData) <- paste0("sample", 1:8)
group <- c(rep("group1", 4), rep("group2", 4))
names(group) <- colnames(arrayData)
result <- differential_array(df = arrayData, group = group)
library(GeoTcgaData)
library(data.table)
# Use real GEO data as example
arrayData <- read.table("GSE54807_series_matrix.txt.gz",
    sep = "\t", header = TRUE,
        fill=TRUE, comment.char = "!", check.names=FALSE)
gpl <- fread("GPL6244-17930.txt", sep = "\t", header = TRUE)
gpl <- gpl[, c("ID", "gene_assignment")]
class(gpl) <- "data.frame"

for (i in seq_len(nrow(gpl))) {
        aa <- strsplit(gpl[i, 2], " // ")[[1]][5]
        gpl[i, 2] <- as.character(strsplit(aa, " /// ")[[1]][1])
}
gpl[,1] <- as.character(gpl[,1])
arrayData[, 1] <- as.character(arrayData[, 1])
rownames(gpl) <- gpl[, 1]
arrayData[, 1] <- gpl[arrayData[, 1], 2]


arrayData <- repRemove(arrayData," /// ")

# Remove rows that do not correspond to genes
arrayData <- arrayData[!is.na(arrayData[, 1]), ]
arrayData <- arrayData[!arrayData[, 1] == "", ]
arrayData <- arrayData[!arrayData[, 1] == "---", ]


arrayData <- arrayData[order(arrayData[, 1]), ]
arrayData <- gene_ave(arrayData, 1)

keep <- apply(arrayData, 1, function(x) sum(x < 1) < (length(x)/2))
arrayData <- arrayData[keep, ]

group <- c(rep("group1", 12), rep("group2", 12))
result <- differential_array(df = arrayData, group = group)

# Use random data as example
arrayData <- matrix(runif(200), 25, 8)
rownames(arrayData) <- paste0("gene", 1:25)
colnames(arrayData) <- paste0("sample", 1:8)
group <- c(rep("group1", 4), rep("group2", 4))
names(group) <- colnames(arrayData)
result <- differential_array(df = arrayData, group = group)

Do difference analysis of gene level copy number variation data

Description

Do difference analysis of gene level copy number variation data

Usage

differential_CNV(
  cnvData,
  sampleGroup,
  method = "Chisquare",
  adjust.method = "BH",
  ...
)
differential_CNV(
  cnvData,
  sampleGroup,
  method = "Chisquare",
  adjust.method = "BH",
  ...
)

Arguments

`cnvData`	data.frame of CNV data, each column is a sample, and each row is a CNV.
`sampleGroup`	vector of sample group
`method`	method to do diffenenital analysis, one of "Chisquare", "fisher", and "CATT"(Cochran-Armitage trend test)
`adjust.method`	adjust.method, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none".
`...`	parameters for "Chisquare", "fisher", and "CATT"(Cochran-Armitage trend test)

Value

data.frame with pvalue and estimate

Examples


# use TCGAbiolinks data as example
library(TCGAbiolinks)
query <- GDCquery(
        project = "TCGA-ACC",
        data.category = "Copy Number Variation",
        data.type = "Gene Level Copy Number",
        access = "open"
)
GDCdownload(query)
cnvData <- GDCprepare(query)
aa <- assays(cnvData)$copy_number
bb <- aa
aa[bb == 2] <- 0
aa[bb < 2] <- -1
aa[bb > 2] <- 1
sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE)
diffCnv <- differential_CNV(aa, sampleGroup)

# Use sangerbox CNV data as example
cnvData <- fread("Merge_GeneLevelCopyNumber.txt")
class(cnvData) <- "data.frame"
rownames(cnvData) <- cnvData[, 1]
cnvData <- cnvData[, -c(1, 2, 3)]
sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE)
diffCnv <- differential_CNV(cnvData, sampleGroup)

# 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)
# use TCGAbiolinks data as example
library(TCGAbiolinks)
query <- GDCquery(
        project = "TCGA-ACC",
        data.category = "Copy Number Variation",
        data.type = "Gene Level Copy Number",
        access = "open"
)
GDCdownload(query)
cnvData <- GDCprepare(query)
aa <- assays(cnvData)$copy_number
bb <- aa
aa[bb == 2] <- 0
aa[bb < 2] <- -1
aa[bb > 2] <- 1
sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE)
diffCnv <- differential_CNV(aa, sampleGroup)

# Use sangerbox CNV data as example
cnvData <- fread("Merge_GeneLevelCopyNumber.txt")
class(cnvData) <- "data.frame"
rownames(cnvData) <- cnvData[, 1]
cnvData <- cnvData[, -c(1, 2, 3)]
sampleGroup <- sample(c("A", "B"), ncol(cnvData), replace = TRUE)
diffCnv <- differential_CNV(cnvData, sampleGroup)

# 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)

differential_limma

Description

differential_limma

Usage

differential_limma(df, group, adjust.method = "BH")
differential_limma(df, group, adjust.method = "BH")

Arguments

`df`	data.frame of the omic data
`group`	a vector, group of samples.
`adjust.method`	adjust.method.

Value

data.frame

Examples

df <- matrix(runif(200), 25, 8)
df <- as.data.frame(df)
rownames(df) <- paste0("gene", 1:25)
colnames(df) <- paste0("sample", 1:8)
group <- sample(c("group1", "group2"), 8, replace = TRUE)
result <- differential_limma(df = df, group = group)
df <- matrix(runif(200), 25, 8)
df <- as.data.frame(df)
rownames(df) <- paste0("gene", 1:25)
colnames(df) <- paste0("sample", 1:8)
group <- sample(c("group1", "group2"), 8, replace = TRUE)
result <- differential_limma(df = df, group = group)

differential_methy

Description

Get methylation difference gene

Usage

differential_methy(
  cpgData,
  sampleGroup,
  groupCol,
  combineMethod = "stouffer",
  missing_value = "knn",
  cpg2gene = NULL,
  normMethod = "PBC",
  region = "TSS1500",
  model = "gene",
  adjust.method = "BH",
  adjPvalCutoff = 0.05,
  ucscData = FALSE
)
differential_methy(
  cpgData,
  sampleGroup,
  groupCol,
  combineMethod = "stouffer",
  missing_value = "knn",
  cpg2gene = NULL,
  normMethod = "PBC",
  region = "TSS1500",
  model = "gene",
  adjust.method = "BH",
  adjPvalCutoff = 0.05,
  ucscData = FALSE
)

Arguments

`cpgData`	data.frame of cpg beta value, , or SummarizedExperiment object
`sampleGroup`	vector of sample group
`groupCol`	group column
`combineMethod`	method to combine the cpg pvalues, a function or one of "stouffer", "fisher" and "rhoScores".
`missing_value`	Method to impute missing expression data, one of "zero" and "knn".
`cpg2gene`	data.frame to annotate cpg locus to gene
`normMethod`	Method to do normalization: "PBC" or "BMIQ".
`region`	region of genes, one of "Body", "TSS1500", "TSS200", "3'UTR", "1stExon", "5'UTR", and "IGR". Only used when cpg2gene is NULL.
`model`	if "cpg", step1: calculate difference cpgs; step2: calculate difference genes. if "gene", step1: calculate the methylation level of genes; step2: calculate difference genes.
`adjust.method`	character string specifying the method used to adjust p-values for multiple testing. See p.adjust for possible values.
`adjPvalCutoff`	adjusted pvalue cutoff
`ucscData`	Logical, whether the data comes from UCSC Xena.

Value

data.frame

Examples


# use TCGAbiolinks data
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-ACC",
    data.category = "DNA Methylation",
    data.type = "Methylation Beta Value",
    platform = "Illumina Human Methylation 450")
GDCdownload(query, method = "api", files.per.chunk = 5,
    directory = Your_Path)
merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data)
library(ChAMP) # To avoid reporting errors
differential_gene <- differential_methy(cpgData = merge_result,
    sampleGroup = sample(c("C","T"),
    ncol(merge_result[[1]]), replace = TRUE))

# 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)  
# use TCGAbiolinks data
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-ACC",
    data.category = "DNA Methylation",
    data.type = "Methylation Beta Value",
    platform = "Illumina Human Methylation 450")
GDCdownload(query, method = "api", files.per.chunk = 5,
    directory = Your_Path)
merge_result <- Merge_methy_tcga(Your_Path_to_DNA_Methylation_data)
library(ChAMP) # To avoid reporting errors
differential_gene <- differential_methy(cpgData = merge_result,
    sampleGroup = sample(c("C","T"),
    ncol(merge_result[[1]]), replace = TRUE))

# 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)

differential_RNA

Description

Do difference analysis of RNA-seq data

Usage

differential_RNA(
  counts,
  group,
  groupCol,
  method = "limma",
  geneLength = NULL,
  gccontent = NULL,
  filter = TRUE,
  edgeRNorm = TRUE,
  adjust.method = "BH",
  useTopconfects = TRUE,
  ucscData = FALSE
)
differential_RNA(
  counts,
  group,
  groupCol,
  method = "limma",
  geneLength = NULL,
  gccontent = NULL,
  filter = TRUE,
  edgeRNorm = TRUE,
  adjust.method = "BH",
  useTopconfects = TRUE,
  ucscData = FALSE
)

Arguments

`counts`	a dataframe or numeric matrix of raw counts data, or SummarizedExperiment object
`group`	sample groups
`groupCol`	group column
`method`	one of "DESeq2", "edgeR" , "limma", "dearseq", "NOISeq", "Wilcoxon", and "auto".
`geneLength`	a vector of gene length.
`gccontent`	a vector of gene GC content.
`filter`	if TRUE, use filterByExpr to filter genes.
`edgeRNorm`	if TRUE, use edgeR to do normalization for dearseq method.
`adjust.method`	character string specifying the method used to adjust p-values for multiple testing. See p.adjust for possible values.
`useTopconfects`	if TRUE, use topconfects to provide a more biologically useful ranked gene list.
`ucscData`	Logical, whether the data comes from UCSC Xena.

Value

data.frame

Examples


library(TCGAbiolinks)

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

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

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

# Use `differential_RNA` to do difference analysis.
# We provide the data of human gene length and GC content in `gene_cov`.
group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE)
library(cqn) # To avoid reporting errors: there is no function "rq"
## get gene length and GC content
library(org.Hs.eg.db)
genes_bitr <- bitr(rownames(gene_cov),
    fromType = "ENTREZID", toType = "ENSEMBL",
    OrgDb = org.Hs.eg.db, drop = TRUE
)
genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ]
gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
rownames(gene_cov2) <- genes_bitr$ENSEMBL
genes <- intersect(rownames(dataPrep), rownames(gene_cov2))
dataPrep <- dataPrep[genes, ]
geneLength <- gene_cov2(genes, "length")
gccontent <- gene_cov2(genes, "GC")
names(geneLength) <- names(gccontent) <- genes
##    Difference analysis
DEGAll <- differential_RNA(
    counts = dataPrep, group = group,
    geneLength = geneLength, gccontent = gccontent
)
# Use `clusterProfiler` to do enrichment analytics:
diffGenes <- DEGAll$logFC
names(diffGenes) <- rownames(DEGAll)
diffGenes <- sort(diffGenes, decreasing = TRUE)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
dotplot(gsego)

# 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") 
library(TCGAbiolinks)

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

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

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

# Use `differential_RNA` to do difference analysis.
# We provide the data of human gene length and GC content in `gene_cov`.
group <- sample(c("grp1", "grp2"), ncol(dataPrep), replace = TRUE)
library(cqn) # To avoid reporting errors: there is no function "rq"
## get gene length and GC content
library(org.Hs.eg.db)
genes_bitr <- bitr(rownames(gene_cov),
    fromType = "ENTREZID", toType = "ENSEMBL",
    OrgDb = org.Hs.eg.db, drop = TRUE
)
genes_bitr <- genes_bitr[!duplicated(genes_bitr[, 2]), ]
gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
rownames(gene_cov2) <- genes_bitr$ENSEMBL
genes <- intersect(rownames(dataPrep), rownames(gene_cov2))
dataPrep <- dataPrep[genes, ]
geneLength <- gene_cov2(genes, "length")
gccontent <- gene_cov2(genes, "GC")
names(geneLength) <- names(gccontent) <- genes
##    Difference analysis
DEGAll <- differential_RNA(
    counts = dataPrep, group = group,
    geneLength = geneLength, gccontent = gccontent
)
# Use `clusterProfiler` to do enrichment analytics:
diffGenes <- DEGAll$logFC
names(diffGenes) <- rownames(DEGAll)
diffGenes <- sort(diffGenes, decreasing = TRUE)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
gsego <- gseGO(gene = diffGenes, OrgDb = org.Hs.eg.db, keyType = "ENSEMBL")
dotplot(gsego)

# 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")

Do difference analysis of SNP data

Description

Do difference analysis of SNP data

Usage

differential_SNP(snpDf, sampleGroup, combineMethod = min)
differential_SNP(snpDf, sampleGroup, combineMethod = min)

Arguments

`snpDf`	data.frame of SNP data, each column is a sample, and each row is a SNP.
`sampleGroup`	vector of sample group.
`combineMethod`	Method of combining the pvalue of multiple snp in a gene.

Value

data.frame

Examples


library(TCGAbiolinks)
query <- GDCquery(
    project = "TCGA-CHOL",
    data.category = "Simple Nucleotide Variation",
    access = "open",
    legacy = FALSE,
    data.type = "Masked Somatic Mutation",
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
data_snp <- GDCprepare(query)
samples <- unique(data_snp$Tumor_Sample_Barcode)
sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE)
names(sampleGroup) <- samples
pvalue <- differential_SNP_tcga(snpData = data_snp, 
    sampleGroup = sampleGroup)

# use demo data
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)
library(TCGAbiolinks)
query <- GDCquery(
    project = "TCGA-CHOL",
    data.category = "Simple Nucleotide Variation",
    access = "open",
    legacy = FALSE,
    data.type = "Masked Somatic Mutation",
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
data_snp <- GDCprepare(query)
samples <- unique(data_snp$Tumor_Sample_Barcode)
sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE)
names(sampleGroup) <- samples
pvalue <- differential_SNP_tcga(snpData = data_snp, 
    sampleGroup = sampleGroup)

# use demo data
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)

Do difference analysis of SNP data downloaded from GEO

Description

Do difference analysis of SNP data downloaded from GEO

Usage

differential_SNP_GEO(snpData, sampleGroup, method = "Chisquare")
differential_SNP_GEO(snpData, sampleGroup, method = "Chisquare")

Arguments

`snpData`	data.frame of SNP data downloaded from GEO
`sampleGroup`	vector of sample group
`method`	one of "Chisquare", "fisher", and "CATT"(Cochran-Armitage trend test)

Value

data.frame

Examples


file1 <- read.table("GSE66903_series_matrix.txt.gz",
    fill=TRUE, comment.char="!", header = TRUE)
rownames(file1) <- file1[, 1]
snpData <- file1[, -1]
sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE)
names(sampleGroup) <- colnames(snpData)
snpData <- SNP_QC(snpData)
sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE)
result1 <- differential_SNP_GEO(snpData = snpData,
    sampleGroup = sampleGroup, method = "Chisquare")

# use demo data
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 <- differential_SNP_GEO(snpDf, sampleGroup, method = "fisher")
file1 <- read.table("GSE66903_series_matrix.txt.gz",
    fill=TRUE, comment.char="!", header = TRUE)
rownames(file1) <- file1[, 1]
snpData <- file1[, -1]
sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE)
names(sampleGroup) <- colnames(snpData)
snpData <- SNP_QC(snpData)
sampleGroup <- sample(c("A", "B"), ncol(snpData ), replace = TRUE)
result1 <- differential_SNP_GEO(snpData = snpData,
    sampleGroup = sampleGroup, method = "Chisquare")

# use demo data
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 <- differential_SNP_GEO(snpDf, sampleGroup, method = "fisher")

Do difference analysis of SNP data downloaded from TCGAbiolinks

Description

Do difference analysis of SNP data downloaded from TCGAbiolinks

Usage

differential_SNP_tcga(snpData, sampleGroup, combineMethod = NULL)
differential_SNP_tcga(snpData, sampleGroup, combineMethod = NULL)

Arguments

`snpData`	data.frame of SNP data downloaded from TCGAbiolinks
`sampleGroup`	vector of sample group
`combineMethod`	Method of combining the pvalue of multiple snp in a gene.

Value

data.frame

Examples


library(TCGAbiolinks)
query <- GDCquery(
    project = "TCGA-CHOL",
    data.category = "Simple Nucleotide Variation",
    access = "open",
    legacy = FALSE,
    data.type = "Masked Somatic Mutation",
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
data_snp <- GDCprepare(query)
samples <- unique(data_snp$Tumor_Sample_Barcode)
sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE)
names(sampleGroup) <- samples
pvalue <- differential_SNP_tcga(snpData = data_snp, 
    sampleGroup = sampleGroup)

# use demo data
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)
library(TCGAbiolinks)
query <- GDCquery(
    project = "TCGA-CHOL",
    data.category = "Simple Nucleotide Variation",
    access = "open",
    legacy = FALSE,
    data.type = "Masked Somatic Mutation",
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
data_snp <- GDCprepare(query)
samples <- unique(data_snp$Tumor_Sample_Barcode)
sampleGroup <- sample(c("A", "B"), length(samples), replace = TRUE)
names(sampleGroup) <- samples
pvalue <- differential_SNP_tcga(snpData = data_snp, 
    sampleGroup = sampleGroup)

# use demo data
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)

Convert fpkm to Tpm

Description

Convert fpkm to Tpm

Usage

fpkmToTpm(fpkm_matrix)
fpkmToTpm(fpkm_matrix)

Arguments

fpkm_matrix

a matrix, colnames of fpkm_matrix are sample name, rownames of fpkm_matrix are genes

Value

a matrix

Examples

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

Average the values of same genes in gene expression profile

Description

Average the values of same genes in gene expression profile

Usage

gene_ave(file_gene_ave, k = 1)
gene_ave(file_gene_ave, k = 1)

Arguments

`file_gene_ave`	a data.frame of gene expression data, each column is a sample, and each row is a gene.
`k`	a number, indicates which is the gene column.

Value

a data.frame, the values of same genes in gene expression profile

Examples

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", "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)

a data.frame of gene length and GC content

Description

the gene length and GC content data comes from TxDb.Hsapiens.UCSC.hg38.knownGene and BSgenome.Hsapiens.UCSC.hg38

Usage

gene_cov
gene_cov

Format

A data.frame with 27341 rows and 2 column

a data.frame of gene expression data

Description

It is a randomly generated expression data used as an example of functions in this package. the rowname is gene symbols the columns are gene expression values

Usage

geneExpress
geneExpress

Format

A data.frame with 10779 rows and 2 column

Get Microarray matrix data from GEO

Description

Get Microarray matrix data from GEO

Usage

get_geo_array(gse)
get_geo_array(gse)

Arguments

gse

GSE number, such as GSE781.

Value

a list of matrix

Examples


arraylist <- get_geo_array("GSE781")

arraylist <- get_geo_array("GSE781")

a matrix of gene expression data in GEO

Description

the first column represents the gene symbol

Usage

GSE66705_sample2
GSE66705_sample2

Format

A matrix with 999 rows and 3 column

Details

the other columns represent the expression of genes

Convert ENSEMBL gene id to gene Symbol in TCGA

Description

Convert ENSEMBL gene id to gene Symbol in TCGA

Usage

id_conversion_TCGA(profiles, toType = "SYMBOL")
id_conversion_TCGA(profiles, toType = "SYMBOL")

Arguments

`profiles`	a data.frame of gene expression data, each column is a sample, and each row is a gene.
`toType`	one of 'keytypes(org.Hs.eg.db)'

Value

a data.frame, gene symbols and their expression value

Examples

library(org.Hs.eg.db)
data(profile)
result <- id_conversion_TCGA(profile)
library(org.Hs.eg.db)
data(profile)
result <- id_conversion_TCGA(profile)

a matrix of gene expression data in TCGA

Description

It is a randomly generated expression data used as an example of functions in this package. the first column represents the gene symbol

Usage

kegg_liver
kegg_liver

Format

A matrix with 100 rows and 150 column

Details

the other columns represent the expression(count) of genes

Merge methylation data downloaded from TCGA

Description

When the methylation data is downloaded from TCGA, each sample is saved in a folder, which contains the methylation value file and the descriptive file. This function can directly extract and consolidate all folders.

Usage

Merge_methy_tcga(dirr = NULL)
Merge_methy_tcga(dirr = NULL)

Arguments

dirr

a string for the directory of methylation data download from tcga useing the tools gdc

Value

a matrix, a combined methylation expression spectrum matrix

Examples

merge_result <- Merge_methy_tcga(system.file(file.path("extdata", "methy"),
    package = "GeoTcgaData"))
merge_result <- Merge_methy_tcga(system.file(file.path("extdata", "methy"),
    package = "GeoTcgaData"))

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

Description

It is a randomly generated expression data used as an example of functions in this package.

Usage

module
module

Format

A matrix with 176 rows and 3 column

Preparer file for chi-square test

Description

Preparer file for chi-square test

Usage

prepare_chi(cnv)
prepare_chi(cnv)

Arguments

cnv

result of ann_merge()

Value

a matrix

Examples

cnv <- matrix(c(
    -1.09150, -1.47120, -0.87050, -0.50880,
    -0.50880, 2.0, 2.0, 2.0, 2.0, 2.0, 2.601962, 2.621332, 2.621332,
    2.621332, 2.621332, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
    2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0
), nrow = 5)
cnv <- as.data.frame(cnv)
rownames(cnv) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3")
colnames(cnv) <- c(
    "TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01",
    "TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01",
    "TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01"
)
cnv_chi_file <- prepare_chi(cnv)
cnv <- matrix(c(
    -1.09150, -1.47120, -0.87050, -0.50880,
    -0.50880, 2.0, 2.0, 2.0, 2.0, 2.0, 2.601962, 2.621332, 2.621332,
    2.621332, 2.621332, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
    2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0
), nrow = 5)
cnv <- as.data.frame(cnv)
rownames(cnv) <- c("AJAP1", "FHAD1", "CLCNKB", "CROCCP2", "AL137798.3")
colnames(cnv) <- c(
    "TCGA-DD-A4NS-10A-01D-A30U-01", "TCGA-ED-A82E-01A-11D-A34Y-01",
    "TCGA-WQ-A9G7-01A-11D-A36W-01", "TCGA-DD-AADN-01A-11D-A40Q-01",
    "TCGA-ZS-A9CD-10A-01D-A36Z-01", "TCGA-DD-A1EB-11A-11D-A12Y-01"
)
cnv_chi_file <- prepare_chi(cnv)

a matrix of gene expression data in TCGA

Description

It is a randomly generated expression data used as an example of functions in this package. the first column represents the gene symbol

Usage

profile
profile

Format

A matrix with 10 rows and 10 column

Details

the other columns represent the expression(FPKM) of genes

Handle the case where one id corresponds to multiple genes

Description

Handle the case where one id corresponds to multiple genes

Usage

repAssign(input_file, string)
repAssign(input_file, string)

Arguments

`input_file`	input file, a data.frame or a matrix, the first column should be genes.
`string`	a string, sep of the gene

Value

a data.frame, when an id corresponds to multiple genes, the expression value is assigned to each gene

Examples

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, " /// ")

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, " /// ")

Handle the case where one id corresponds to multiple genes

Description

Handle the case where one id corresponds to multiple genes

Usage

repRemove(input_file, string)
repRemove(input_file, string)

Arguments

`input_file`	input file, a data.frame or a matrix, the first column should be genes.
`string`	a string,sep of the gene

Value

a data.frame, when an id corresponds to multiple genes, the expression value is deleted

Examples

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)
repRemove_result <- repRemove(input_file, " /// ")
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)
repRemove_result <- repRemove(input_file, " /// ")

Do quality control of SNP data downloaded from TCGAbiolinks

Description

Do quality control of SNP data downloaded from TCGAbiolinks

Usage

SNP_QC(
  snpData,
  geon = 0.02,
  mind = 0.02,
  maf = 0.05,
  hwe = 1e-06,
  miss = "NoCall"
)
SNP_QC(
  snpData,
  geon = 0.02,
  mind = 0.02,
  maf = 0.05,
  hwe = 1e-06,
  miss = "NoCall"
)

Arguments

`snpData`	data.frame of SNP data downloaded from TCGAbiolinks
`geon`	filters out all variants with missing call rates exceeding the provided value (default 0.02) to be removed
`mind`	filters out all samples with missing call rates exceeding the provided value (default 0.02) to be removed
`maf`	filters out all variants with minor allele frequency below the provided threshold
`hwe`	filters out all variants which have Hardy-Weinberg equilibrium exact test p-value below the provided threshold
`miss`	character of miss value

Value

data.frame

Examples

# use demo data
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)
# use demo data
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)

a matrix of gene expression data in GEO

Description

It is a randomly generated expression data used as an example of functions in this package. the first column represents the gene symbol

Usage

ventricle
ventricle

Format

A matrix with 32 rows and 20 column

Details

the other columns represent the expression of genes

Package 'GeoTcgaData'

Help Index

Preprocess of Microarray data

Description

Usage

Arguments

Value

Examples

Find the mean value of the gene in each module

Description

Usage

Arguments

Value

Examples

cluster probes of Microarray data

Description

Usage

Arguments

Value

Examples

combine pvalues of SNP difference analysis result

Description

Usage

Arguments

Value

Examples

Convert count to FPKM

Description

Usage

Arguments

Value

Examples

Convert count to Tpm

Description

Usage

Arguments

Value

Examples

Differential analysis of Microarray data

Description

Usage

Arguments

Value

Examples

Do difference analysis of gene level copy number variation data

Description

Usage

Arguments

Value

Examples

differential_limma

Description

Usage

Arguments

Value

Examples

differential_methy

Description

Usage

Arguments

Value

Examples

differential_RNA

Description

Usage

Arguments

Value

Examples

Do difference analysis of SNP data

Description

Usage

Arguments

Value

Examples

Do difference analysis of SNP data downloaded from GEO

Description

Usage

Arguments

Value

Examples