twilight.pval {twilight} | R Documentation |
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
.
twilight.pval(xin, yin, method = "fc", paired = FALSE, B = 1000, yperm = NULL, balance = FALSE, quant.ci = 0.95, s0=NULL, verbose = TRUE)
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. |
See vignette.
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.
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
.
Stefanie Scheid http://www.molgen.mpg.de/~scheid
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.
twilight
, plot.twilight
, twilight.combi
### 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")