twilight.pval {twilight} | R Documentation

## Compute p-values from expression sets

### 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)

[Package twilight version 1.64.0 Index]