fcros (version 1.6.1)

fcrosTtest: Student t-test for detecting differentially expressed genes

Description

The function uses the basic R t.test() function to perform the Student t-test. It should be used for two biological conditions dataset (microarray, or RNA-seq). The Fold changes, statistics and p-values are returned for each gene in the dataset.

Usage

fcrosTtest(xdata, cont, test, log2.opt = 0)

Arguments

xdata

A table containing a two biological conditions dataset to process for detecting differentially expressed genes. The rownames of xdata are used for the output idnames.

cont

A vector containing the label names of the control samples: cont = c("cont01", "cont02", ...)

test

A vector containing the label names of the test samples: test = c("test01", "test02", "test03", ...)

log2.opt

A scalar equals to 0 or 1. The value 0 (default) means that data in the matrix "xdata" are expressed in a log2 scale: log2.opt = 0

Value

idnames

A vector containing the list of IDs or symbols associated with genes

FC

The fold changes for the genes in the dataset.

stat

The Student t-test statistics associated with genes.

p.value

The Student t-test p-values associated with genes.

Details

Label names appearing in the parameters "cont" and "test" should match column label names of the data matrix "xdata". It is not necessary to use all column label names of the dataset "xdata".

Examples

# NOT RUN {
   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcrosTtest()
   at <- fcrosTtest(fdata, cont, test, log2.opt);
   
   # now select some differentially expressed genes
   id.de  <- matrix(0, 1);
   n <- length(at$FC);
   for (i in 1:n) {
       if ((at$p.value)[i] <= 0.0005) { id.de <- rbind(id.de, i); }
   }

   data.de <- fdata[id.de, ];
   nde <- nrow(data.de);

   # now plot the DE genes
   t <- 1:20;
   plot(t, data.de[1, 2:21], type = "l", col = "blue", xlim = c(1,20),
        ylim = c(0,18), main = "Down- and up-regulated genes");
   for (i in 2:nde) {
       lines(t, data.de[i,2:21], type = "l", col = "blue")
   }
# }