DoTest: Apply two-sample t-test for each row of input data matrix.
In YC3/mirNet: microRNA netowrk analyses

Description Usage Arguments Details Value See Also Examples

DoTest applies two-sample t-test for each row of input data matrix. P-values are adjusted using Benjamini–Hochberg procedure.

1	DoTest(data, pheno)

`data`	A matrix, the normalized gene/microRNA expression dataset, should be a numeric matrix, with rows referring to genes/microRNAs and columns to samples.
`pheno`	A vector of sample phenotypes. Sample phenotype in a scientific research could be treatment/control, normal/cancer or smoker/non-smoker. Different phenotypes should each be encoded as 0/1 when inputting to `DoTest`, for example, Normal-0; Cancer-1.

This function computes the t-statistic from two-sample comparisons which assumes normality in data. If sample size is too small, it is better to perform a moderated t-test using empirical Bayes method (eBayes).

A table with rows for all genes (ranked by significance) and columns of t-statistic, p-value, adjusted p-value (default to Benjamini–Hochberg procedure).

pairComp for applying a moderated t-test for input data matrix, eBayes for Bayes method.

# prepare your normalized data matrix
data.m <- matrix(rnorm(120), nrow = 20, ncol = 6)

# prepare the phenotype info (0-control; 1-treatment)
class.v <- c(0, 0, 0, 1, 1, 1)

# run function
DoTest(data = data.m, pheno = class.v)