ibb.test {countdata}

The inverted beta-binomial test

Description

Performs the inverted beta-binomial test for paired count data.

Usage

ibb.test(x, tx, group, alternative = c("two.sided", "less", "greater"),
         n.threads = -1, BIG = 1e4, verbose = TRUE)

Arguments

x

A vector or matrix of counts. When x is a matrix, the test is performed row by row.

tx

A vector or matrix of the total sample counts. When tx is a matrix, the number of rows must be equal to the number of rows of x.

group

A vector of group indicators. There should be two groups of equal size. The samples are matched by the order of appearance in each group.

alternative

A character string specifying the alternative hypothesis: "two.sided" (default), "greater" or "less".

n.threads

The number of threads to be used. When n.threads is 0, the maximal number of CPU cores is used. When n.threads is -1 (default), one CPU core less than the maximum is used, and so on.

BIG

A number representing a big value of the result, i.e. black-and-white regulation.

verbose

A logical value. If TRUE (default), status information is printed.

Details

This test is designed for paired samples, for example data acquired before and after treatment.

Value

A list of values is returned

p.value

The p-value of the test.

fc

An estimation of the common fold change for all sample pairs. A positive value means up-regulation, i.e. the second group is higher, and a negative value down-regulation. A black-and-white regulation is denoted by the BIG value.

Author(s)

Thang V. Pham

References

Pham TV, Jimenez CR (2012) An accurate paired sample test for count data. Bioinformatics, 28(18):i596-i602.

Examples

x <- c(33, 32, 86, 51, 52, 149)

tx <- c(7742608, 15581382, 20933491, 7126839, 13842297, 14760103)

group <- c(rep("cancer", 3), rep("normal", 3))

ibb.test(x, tx, group)
# p.value = 0.004103636
# fc = 2.137632