chromosomeStats: Find aberrations with whole-chromosome resolution

In CAFE: Chromosmal Aberrations Finder in Expression data

Description

Calculate significant chromosomes with various statistical tests

Usage

chromosomeStats(datalist, chromNum=1, samples=NULL, select="cli", test="fisher",
bonferroni = TRUE, enrichment = "greater")

Arguments

datalist	The CAFE datalist to be analyzed, i.e. the output of ProcessCels.
chromNum	The chromosome to be calculated. This can be "ALL" to calculate all chromosomes.
samples	A vector containing sample numbers to be analyzed
select	Signifies which type of sample selection prompt will be shown, if samples=NULL. Currently supported are "cli" for a command line interface and "gui" for a tcl/tk-based graphical user interface.
test	Signifies which statistical test to be used in the final calculation. Must be either "fisher" for an exact fisher test or "chisqr" for a chi square test.
bonferroni	If bonferroni=TRUE, will correct the p-values of the enrichment test with a bonferroni method.
enrichment	Test for over or underexpression. Can be set to "greater" or "less".

Value

A named vector containing p-values.

Note

Technically speaking, the Fisher's exact test is better than the chi-square test; the Fisher's exact test gives an exact p-value, whereas the chi-square test only gives an approximation. However, the Fisher's exact test can get slow for large sample sizes, and the chi-square test becomes better with increasing sample size but does not slow down as much.

Author(s)

Sander Bollen

See Also

bandStats armStats

Examples

data("CAFE_data")
sam <- c(9,11)
chromosomeStats(CAFE_data,chromNum=17,samples=sam,test="fisher")

CAFE documentation built on Nov. 8, 2020, 7:44 p.m.