twilight.teststat {twilight} | R Documentation |
A function to compute two-sample t, Z and fold change equivalent test statistics (paired or unpaired) and correlation coefficients.
twilight.teststat(xin, yin, method = "fc", paired = FALSE, s0 = NULL)
xin |
Either an expression set ( |
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, |
method |
Character string: |
paired |
Logical value. Depends on whether the samples are paired. Ignored if |
s0 |
Fudge factor for variance correction in the Z-test. Takes effect only if |
Please see vignette for detailed information.
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.
Stefanie Scheid
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.
### 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")