twilight.pval {twilight} | R Documentation

## A function to compute p-values from expression sets

### Description

The functions implements twosample t, Z and fold change equivalent tests, 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)

### Arguments

`xin` | Either an expression set (`exprSet`) 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. If `yperm` is specified, no other permutation will be done. Ignored if `method="pearson"` or `method="spearman"`.
`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 Z test. Takes effect only if `method="z"`. If `s0=NULL`: Fudge factor is set to median of root pooled variances.
`verbose` | Logical value for message printing.

### Details

See vignette.

### 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` | Twosided test permutation p-values.
`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.
`call` | Character string of function arguments.
`quant.ci` | Passes `quant.ci` for plotting.

The remaining slots are left free for function `twilight`.

### Author(s)

Stefanie Scheid http://www.molgen.mpg.de/~scheid

### References

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD and Lander ES (1999): Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science 286, 531–537.

Huber W, von Heydebreck A, Sültmann H, Poustka A and Vingron M (2002): Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics 18, suppl. 1, S96–S104.

Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, IEEE TCBB 1(3), 98–108.

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`

### Examples

    ### Leukemia data set of Golub et al. (1999)
    library(golubEsets)
    data(golubMerge)

    ### Variance-stabilizing normalization of Huber et al. (2002)
    library(vsn)
    golubNorm <- vsn(exprs(golubMerge))

    ### A vector of class labels.
    id <- as.numeric(golubMerge$ALL.AML)

    a <- twilight.pval(golubNorm,id)
    plot(a,which="scores")
    plot(a,which="qvalues")

[Package twilight version 1.1.0 Index]