sam {siggenes} | R Documentation

## Significance Analysis of Microarray

### Description

Performs a Significance Analysis of Microarrays (SAM). It is possible to perform one and two class analyses using either a modified t-statistic or a (standardized) Wilcoxon rank statistic, and a multiclass analysis using a modified F-statistic. Moreover, this function provides a SAM procedure for categorical data such as SNP data and the possibility to employ an user-written score function.

### Usage

      sam(data, cl, method = d.stat, control=samControl(),
          gene.names = dimnames(data)[[1]], ...)

### Arguments

`data` | a matrix, a data frame, or an ExpressionSet object. Each row of `data` (or `exprs(data)`, respectively) must correspond to a variable (e.g., a gene), and each column to a sample (i.e.\ an observation). Can also be a list (if `method = chisq.stat` or `method = trend.stat`). For details on how to specify data in this case, see `chisq.stat`.
`cl` | a vector of length `ncol(data)` containing the class labels of the samples. In the two class paired case, `cl` can also be a matrix with `ncol(data)` rows and 2 columns. If `data` is an ExpressionSet object, `cl` can also be a character string naming the column of `pData(data)` that contains the class labels of the samples. If `data` is a list, `cl` needs not to be specified. In the one-class case, `cl` should be a vector of 1's. In the two class unpaired case, `cl` should be a vector containing 0's (specifying the samples of, e.g., the control group) and 1's (specifying, e.g., the case group). In the two class paired case, `cl` can be either a numeric vector or a numeric matrix. If it is a vector, then `cl` has to consist of the integers between -1 and -n/2 (e.g., before treatment group) and between 1 and n/2 (e.g., after treatment group), where n is the length of `cl` and k is paired with -k, k=1,…,n/2. If `cl` is a matrix, one column should contain -1's and 1's specifying, e.g., the before and the after treatment samples, respectively, and the other column should contain integer between 1 and n/2 specifying the n/2 pairs of observations. In the multiclass case and if `method = chisq.stat`, `cl` should be a vector containing integers between 1 and g, where g is the number of groups. (In the case of `chisq.stat`, `cl` needs not to be specified if `data` is a list of groupwise matrices.) For examples of how `cl` can be specified, see the manual of siggenes.
`method` | a character string or a name specifying the method/function that should be used in the computation of the expression scores d. If `method = d.stat`, a modified t-statistic or F-statistic, respectively, will be computed as proposed by Tusher et al. (2001). If `method = wilc.stat`, a Wilcoxon rank sum statistic or Wilcoxon signed rank statistic will be used as expression score. For an analysis of categorical data such as SNP data, `method` can be set to `chisq.stat`. In this case Pearson's ChiSquare statistic is computed for each row. If the variables are ordinal and a trend test should be applied (e.g., in the two-class case, the Cochran-Armitage trend test), `method = trend.stat` can be employed. It is also possible to use an user-written function to compute the expression scores. For details, see `Details`.
`control` | further optional arguments for controlling the SAM analysis. For these arguments, see `samControl`.
`gene.names` | a character vector of length `nrow(data)` containing the names of the genes. By default the row names of `data` are used.
`...` | further arguments of the specific SAM methods. If `method = d.stat`, see the help of `d.stat`. If `method = wilc.stat`, see the help of `wilc.stat`. If `method = chisq.stat`, see the help of `chisq.stat`.

### Details

`sam` provides SAM procedures for several types of analysis (one and two class analyses with either a modified t-statistic or a Wilcoxon rank statistic, a multiclass analysis with a modified F statistic, and an analysis of categorical data). It is, however, also possible to write your own function for another type of analysis. The required arguments of this function must be `data` and `cl`. This function can also have other arguments. The output of this function must be a list containing the following objects:

`d`:

  a numeric vector consisting of the expression scores of the genes.

`d.bar`:

  a numeric vector of the same length as `na.exclude(d)` specifying the expected expression scores under the null hypothesis.

`p.value`:

  a numeric vector of the same length as `d` containing the raw, unadjusted p-values of the genes.

`vec.false`:

  a numeric vector of the same length as `d` consisting of the one-sided numbers of falsely called genes, i.e. if d > 0 the numbers of genes expected to be larger than d under the null hypothesis, and if d<0, the number of genes expected to be smaller than d under the null hypothesis.

`s`:

  a numeric vector of the same length as `d` containing the standard deviations of the genes. If no standard deviation can be calculated, set `s = numeric(0)`.

`s0`:

  a numeric value specifying the fudge factor. If no fudge factor is calculated, set `s0 = numeric(0)`.

`mat.samp`:

  a matrix with B rows and `ncol(data)` columns, where B is the number of permutations, containing the permutations used in the computation of the permuted d-values. If such a matrix is not computed, set `mat.samp = matrix(numeric(0))`.

`msg`:

  a character string or vector containing information about, e.g., which type of analysis has been performed. `msg` is printed when the function `print` or `summary`, respectively, is called. If no such message should be printed, set `msg = ""`.

`fold`:

  a numeric vector of the same length as `d` consisting of the fold changes of the genes. If no fold change has been computed, set `fold = numeric(0)`.

If this function is, e.g., called `foo`, it can be used by setting `method = foo` in `sam`. More detailed information and an example will be contained in the siggenes manual.

### Value

An object of class SAM.

### Author(s)

Holger Schwender, holger.schw@gmx.de

### References

Schwender, H., Krause, A., and Ickstadt, K. (2006). Identifying Interesting Genes with siggenes. RNews, 6(5), 45-50.

Schwender, H. (2004). Modifying Microarray Analysis Methods for Categorical Data – SAM and PAM for SNPs. To appear in: Proceedings of the the 28th Annual Conference of the GfKl.

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

`SAM-class`,`d.stat`,`wilc.stat`, `chisq.stat`, `samControl`

### Examples

    ## Not run:
      # Load the package multtest and the data of Golub et al. (1999)
      # contained in multtest.
      library(multtest)
      data(golub)

      # golub.cl contains the class labels.
      golub.cl

      # Perform a SAM analysis for the two class unpaired case assuming
      # unequal variances.
      sam.out <- sam(golub, golub.cl, B=100, rand=123)
      sam.out

      # Obtain the Delta plots for the default set of Deltas
      plot(sam.out)

      # Generate the Delta plots for Delta = 0.2, 0.4, 0.6, ..., 2
      plot(sam.out, seq(0.2, 0.4, 2))

      # Obtain the SAM plot for Delta = 2
      plot(sam.out, 2)

      # Get information about the genes called significant using
      # Delta = 3.
      sam.sum3 <- summary(sam.out, 3, entrez=FALSE)

      # Obtain the rows of golub containing the genes called
      # differentially expressed
      sam.sum3@row.sig.genes

      # and their names
      golub.gnames[sam.sum3@row.sig.genes, 3]

      # The matrix containing the d-values, q-values etc. of the
      # differentially expressed genes can be obtained by
      sam.sum3@mat.sig

      # Perform a SAM analysis using Wilcoxon rank sums
      sam(golub, golub.cl, method="wilc.stat", rand=123)

      # Now consider only the first ten columns of the Golub et al. (1999)
      # data set. For now, let's assume the first five columns were
      # before treatment measurements and the next five columns were
      # after treatment measurements, where column 1 and 6, column 2
      # and 7, ..., build a pair. In this case, the class labels
      # would be
      new.cl <- c(-(1:5), 1:5)
      new.cl

      # and the corresponding SAM analysis for the two-class paired
      # case would be
      sam(golub[,1:10], new.cl, B=100, rand=123)

      # Another way of specifying the class labels for the above paired
      # analysis is
      mat.cl <- matrix(c(rep(c(-1, 1), e=5), rep(1:5, 2)), 10)
      mat.cl

      # and the above SAM analysis can also be done by
      sam(golub[,1:10], mat.cl, B=100, rand=123)

    ## End(Not run)

[Package siggenes version 1.64.0 Index]