twilight.pval: Compute p-values from expression sets

In twilight: Estimation of local false discovery rate

Description

A function to compute two-sample t, Z and fold change equivalent test statistics (paired or unpaired) and correlation coefficients. Based on permutations, expected test statistics as given in Tusher et al. (2001) and empirical p-values are computed. Additional output are q-values computed as given in Storey and Tibshirani (2003). The resulting object is of class twilight and can be passed to functions twilight or plot.twilight.

Usage

twilight.pval(xin, yin, method = "fc", paired = FALSE, B = 1000, yperm = NULL, balance = FALSE, quant.ci = 0.95, s0=NULL, verbose = TRUE, filtering = FALSE)

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. 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".
B	Numerical value specifying the number of permutations.
yperm	Optional matrix containing in each row a permutation of the class labels in binary(!) format for two-sample testing. For computation of correlation scores, the rows of yperm have to contain the appropriate values or ranks. Use this argument carefully! If yperm is specified, no other permutation will be done. Please note that the first row of yperm MUST be the input vector yin. Otherwise, the p-value calculation will be incorrect.
balance	Logical value. Depends on whether balanced or unbalanced permutations should be done. Ignored if method="pearson" or method="spearman".
quant.ci	Probability value for confidence lines. Lines are symmetric and denote the quant.ci-quantile of maximal absolute differences between each permutatin and the expected scores.
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.
verbose	Logical value for message printing.
filtering	Logical value for filtering for permutations of class labels that produce a set of complete null scores. Invokes function twilight.filtering. If yperm is specified, no filtering will be done. Note that the filtering is done on unbalanced permutations even if balance=TRUE.

Details

Please see vignette for detailed information.

Value

Returns a twilight object consisting of a data.frame named result with variables

observed	Observed test statistics.
expected	Mean of order statistics of the permutation statistics.
candidate	Binary vector. "1" for observations exceeding the confidence lines.
pvalue	Empirical p-values from two-sided hypothesis tests.
qvalue	q-values computed as described in Storey and Tibshirani (2003).
index	Index of the original ordering.

Values are sorted by absolute observed scores.

Note

Additional output consists of

ci.line	Quantile corresponding to quant.ci, passed for plotting.
pi0	Estimated prior probability.
s0	Estimated fudge factor if method="z".
call	Character string of function arguments.
quant.ci	Passes quant.ci for plotting.

The remaining slots are left empty for function twilight.

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.

Storey JD and Tibshirani R (2003): Statistical significance for genomewide studies, PNAS 100(16), 9440–9445.

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, plot.twilight, twilight.combi, twilight.filtering, expval, twilight.teststat

Examples

### twilight object created from Golub data set
data(expval)
print(expval)
plot(expval)

twilight documentation built on Nov. 8, 2020, 5:38 p.m.