chromosomeStats {CAFE} | R Documentation

## Find aberrations with whole-chromosome resolution

### 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")

[Package CAFE version 1.23.0 Index]