countdata (version 1.3)

ibb.test: 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)

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.

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.

Author

Thang V. Pham

Details

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

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