comp.SAM {DEDS} | R Documentation

## Computing SAM Statistics for Differential Expression

### Description

`comp.SAM` returns a function of one argument. This function has a environment with bindings for a series of arguments (see below). It accepts a microarray data matrix as its single argument, when evaluated, computes SAM statistics for each row of the matrix.

### Usage

    comp.SAM(L = NULL, prob = 0.5, B = 200, stat.only = TRUE, verbose = FALSE,
    deltas, s.step=0.01, alpha.step=0.01, plot.it=FALSE)

### Arguments

`L` | A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1.
`prob` | A numeric variable used to set the fudge factor s_0 in terms of the percentile of the standard deviations of the genes. If set as `NULL`, s_0 is calculated using the algorithm by Tusher et al. (see reference).
`B` | The number of permutations. For a complete enumeration, `B` should be 0 (zero) or any number not less than the total number of permutations.
`stat.only` | A logical variable, if `TRUE`, only statistics are calculated and returned; if `FALSE`, false discovery rates (FDRs) for a set of delta(`deltas`) are calculated and returned.
`verbose` | A logical variable, if `TRUE`, informative messages are printed during the computation process.
`deltas` | A vector of values for the threshold delta; see Tusher et al.
`s.step` | A numeric variable specifying the size of the moving window across the gene-wise standard deviations for the selection of the fudge factor s_0.
`alpha.step` | A numeric variable specifying the increment of a percentile sequence between 0 and 1, from which the fudge factor will be chosen to minimize the coefficient of variation of statistics.
`plot.it` | A logical variable, if `TRUE`, a plot between the coefficient of variation and the percentile sequence will be made.

### Details

The function returned by `comp.SAM` calculates SAM statistics for each row of the microarray data matrix, with bindings for `L`, `prob`, `B`, `stat.only`, `verbose`, `deltas`, `s.step`, `alpha.step` and `plot.it`. If `quantile=NULL`, the fudge factor s_0 is calculated as the percentile of the gene-wise standard deviations that minimizes the coefficient of variation of the statistics; otherwise s_0 is set as the specified percentile of standard deviations. If `stat.only=T`, only SAM statistics are returned; otherwise, permutation will be carried out to calculate the FDRs for a set of `deltas` specified and a FDR table will be returned in addition to the SAM statistics.

### Value

`SAM` returns a function (F) with bindings for a series of arguments. When `stat.only=T`, the function F when evaluated returns a numeric vector of SAM statistics; When `stat.only=F`, the function F when evaluated returns a list of the following components:

`geneOrder` | Order of genes in terms of differential expression;
`sam` | Sorted SAM statistics;
`fdr.table` | A matrix with columns: delta, no.significance, no.positive, no.negatvie, FDR(50%), FDR(90%).

### Author(s)

Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.

### References

Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, PNAS, 98, 5116-5121.

### See Also

`comp.t`

### Examples

    X <- matrix(rnorm(1000,0,0.5), nc=10)
    L <- rep(0:1,c(5,5))

    # genes 1-10 are differentially expressed
    X[1:10,6:10]<-X[1:10,6:10]+1

    # two sample test, statistics only
    sam.fun <- comp.SAM(L)
    sam.X <- sam.fun(X)

    # two sample test, FDR
    sam.fun <- comp.SAM(L, stat.only=FALSE, delta=c(0.1, 0.2, 0.5))
    sam.X <- sam.fun(X)

[Package DEDS version 1.56.0 Index]