twilight.teststat {twilight} | R Documentation

## Interface to the test statistics provided within 'twilight'

### Description

A function to compute two-sample t, Z and fold change equivalent test statistics (paired or unpaired) and correlation coefficients.

### Usage

    twilight.teststat(xin, yin, method = "fc", paired = FALSE, s0 = NULL)

### Arguments

`xin` | Either an expression set (`ExpressionSet`) or a data matrix with rows corresponding to features and columns corresponding to samples.
`yin` | A numerical vector containing class labels. The higher label denotes the case, the lower label the control samples to test case vs. control. For correlation scores, `yin` can be any numerical vector of length equal to the number of samples.
`method` | Character string: `"fc"` for fold change equivalent test (that is log ratio test), `"t"` for t-test, and `"z"` for Z-test of Tusher et al. (2001). With `"pearson"` or `"spearman"`, the test statistic is either Pearson's correlation coefficient or Spearman's rank correlation coefficient.
`paired` | Logical value. Depends on whether the samples are paired. Ignored if `method="pearson"` or `method="spearman"`.
`s0` | Fudge factor for variance correction in the Z-test. Takes effect only if `method="z"`. If `s0=NULL`: The fudge factor is set to the median of the pooled standard deviations.

### Details

Please see vignette for detailed information.

### Value

Returns a list with two components: a numerical vector of observed test statistics `observed`. Each entry corresponds to one row of the input data matrix. Also, the estimated fudge factor `s0` is returned. In any other case except `method="z"`, `s0` is zero.

### Author(s)

Stefanie Scheid

### References

Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, IEEE TCBB 1(3), 98–108.

Scheid S and Spang R (2005): twilight; a Bioconductor package for estimating the local false discovery rate, Bioinformatics 21(12), 2921–2922.

Scheid S and Spang R (2006): Permutation filtering: A novel concept for significance analysis of large-scale genomic data, in: Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M (Eds.): Research in Computational Molecular Biology: 10th Annual International Conference, Proceedings of RECOMB 2006, Venice, Italy, April 2-5, 2006. Lecture Notes in Computer Science vol. 3909, Springer, Heidelberg, pp. 338-347.

Tusher VG, Tibshirani R and Chu G (2001): Significance analysis of mircroarrays applied to the ionizing response, PNAS 98(9), 5116–5121.

### See Also

`twilight.pval`

### Examples

    ### Z-test on random values
    M <- matrix(rnorm(20000),nrow=1000)
    id <- c(rep(1,10),rep(0,10))
    stat <- twilight.teststat(M,id,method="z")

    ### Pearson correlation
    id <- 1:20
    stat <- twilight.teststat(M,id,method="pearson")

[Package twilight version 1.64.0 Index]