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.

Usage

  sam(data, cl, method = "d.stat", delta = NULL, n.delta = 10, p0 = NA,
      lambda = seq(0, 0.95, 0.05), ncs.value = "max", ncs.weights = NULL,
      gene.names = dimnames(data)[[1]], q.version = 1, ...)

Arguments

data a matrix, data frame or exprSet object. Each row of data (or exprs(data), respectively) must correspond to a gene, and each column to a sample
cl a numeric 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 a exprSet object, cl can also be a character string naming the column of pData(data) that contains the class labels of the samples.
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 vector or a 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="cat.stat", cl should be a vector containing integers between 1 and g, where g is the number of groups.
For examples of how cl can be specified, see the manual of siggenes
method a character string specifying the method 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 "cat.stat". In this case Pearson's Chi-squared statistic is computed for each row. It is also possible to use a user-written function to compute the expression scores. For details, see Details
delta a numeric vector specifying a set of values for the threshold Delta that should be used. If NULL, n.delta Delta values will be computed automatically
n.delta a numeric value specifying the number of Delta values that will be computed over the range of all possible values for Delta if delta is not specified
p0 a numeric value specifying the prior probability pi0 that a gene is not differentially expressed. If NA, p0 will be computed by the function pi0.est
lambda a numeric vector or value specifying the lambda values used in the estimation of the prior probability. For details, see ?pi0.est
ncs.value a character string. Only used if lambda is a vector. Either "max" or "paper". For details, see ?pi0.est
ncs.weights a numerical vector of the same length as lambda containing the weights used in the estimation of pi0. By default no weights are used. For details, see ?pi0.est
gene.names a character vector of length nrow(data) containing the names of the genes. By default the row names of data are used
q.version a numeric value indicating which version of the q-value should be computed. If q.version=2, the original version of the q-value, i.e. min{pFDR}, will be computed. If q.version=1, min{FDR} will be used in the calculation of the q-value. Otherwise, the q-value is not computed. For details, see ?qvalue.cal
... further arguments of the specific SAM methods. If method="d.stat", see ?sam.dstat, if method="wilc.stat", see ?sam.wilc, and if method="cat.stat", see ?sam.snp for these arguments

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

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

Note

SAM was deveoped by Tusher et al. (2001).

!!! There is a patent pending for the SAM technology at Stanford University. !!!

Author(s)

Holger Schwender, holger.schw@gmx.de

References

Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of the Empirical Bayes and the Significance Analysis of Microarrays. Technical Report, SFB 475, University of Dortmund, Germany. http://www.sfb475.uni-dortmund.de/berichte/tr44-03.pdf.

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,sam.dstat,sam.wilc, sam.snp,sam.plot2,delta.plot

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 (since neither the gene names nor the chip type
  # has been specified ll is set to FALSE to avoid a warning)
  sam.sum3<-summary(sam.out,3,ll=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.out@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 1.4.0 Index]