differential_array

differential_array

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

Arguments

df

data.frame of the omic data

group

a vector, group of samples.

method

one of "limma", "ttest", "wilcox"

adjust.method

adjust.method.

Value

data.frame

Examples

if (FALSE) {
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 1: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))
result <- differential_array(df = arrayData, group = group)
}