R: Interface to the test statistics provided within 'twilight'

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 http://www.molgen.mpg.de/~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.

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 1.58.0 Index]