twilight.teststat(xin, yin, method = "fc", paired = FALSE, s0 = NULL)
ExpressionSet
) or a data matrix with rows corresponding to features and columns corresponding to samples. yin
can be any numerical vector of length equal to the number of samples. "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.method="pearson"
or method="spearman"
.method="z"
. If s0=NULL
: The fudge factor is set to the median of the pooled standard deviations.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.
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.
twilight.pval
### 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")
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