Class SAM

Description

This is a class representation for several versions of the SAM (Significance Analysis of Microarrays) procedure proposed by Tusher et al. (2001).

Objects from the Class

Objects can be created using the functions sam, sam.dstat, sam.wilc and sam.snp.

Slots

d:

Object of class "numeric" representing the expression scores of the genes.

d.bar:

Object of class "numeric" representing the expected expression scores under the null hypothesis.

vec.false:

Object of class "numeric" containing the one-sided expected number of falsely called genes.

p.value:

Object of class "numeric" consisting of the p-values of the genes.

s:

Object of class "numeric" representing the standard deviations of the genes. If the standard deviations are not computed, s will be set to numeric(0).

s0:

Object of class "numeric" representing the value of the fudge factor. If not computed, s0 will be set to numeric(0).

mat.samp:

Object of class "matrix" containing the permuted group labels used in the estimation of the null distribution. Each row represents one permutation, each column one observation (pair). If no permutation procedure has been used, mat.samp will be set to matrix(numeric(0)).

p0:

Object of class "numeric" representing the prior probability that a gene is not differentially expressed.

mat.fdr:

Object of class "matrix" containing general information as the number of significant genes and the estimated FDR for several values of Delta. Each row represents one value of Delta, each of the 9 columns one statistic.

q.value:

Object of class "numeric" consisting of the q-values of the genes. If not computed, q.value will be set to numeric(0).

fold:

Object of class "numeric" representing the fold changes of the genes. If not computed, fold will be set to numeric(0).

msg:

Object of class "character" containing information about, e.g., the type of analysis. msg is printed when the functions print and summary, respectively, are called.

chip:

Object of class "character" naming the microarray used in the analysis. If no information about the chip is available, chip will be set to "".

Methods

identify

signature(x = "SAM"): After generating a SAM plot, identify can be used to obtain information about the genes by clicking on the symbols in the SAM plot. For details, see help.sam(identify). Arguments are listed by args.sam(identify).

plot

signature(x = "SAM"): Generates a SAM plot or the Delta plots. If the specified delta in plot(object,delta) is a numeric value, a SAM plot will be generated. If delta is either not specified or a numeric vector, the Delta plots will be generated. For details, see ?sam.plot2, ?delta.plot or help.sam(plot),respectively. Arguments are listed by args.sam(plot).

print

signature(x = "SAM"): Prints general information such as the number of significant genes and the estimated FDR for a set of Delta. For details, see help.sam(print). Arguments are listed by args.sam(print).

show

signature(object = "SAM"): Shows the output of the SAM analysis.

summary

signature(object = "SAM"): Summarizes the results of a SAM analysis. If delta in summary(object,delta) is not specified or a numeric vector, the information shown by print and some additional information will be shown. If delta is a numeric vector, the general information for the specific Delta is shown and additionally gene-specific information about the genes called significant using this value of Delta. The output of summary is an object of class sumSAM which has the slots row.sig.genes, mat.fdr, mat.sig and list.args. For details, see help.sam(summary). All arguments are listed by args.sam(summary).

Note

SAM was developed 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,args.sam,sam.plot2, delta.plot

Examples

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## Not run: 
  # Load the package multtest and the data of Golub et al. (1999)
  # contained in multtest.
  library(multtest)
  data(golub)
  
  # 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
  
  # Alternative ways to show the output of sam.
  show(sam.out)
  print(sam.out)
  
  # Obtain a little bit more information.
  summary(sam.out)
  
  # Print the results of the SAM analysis for other values of Delta.
  print(sam.out, seq(.2, 2, .2))
  
  # Again, the same with additional information.
  summary(sam.out, seq(.2, 2, .2))
    
  # 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)
  sam.sum3
  
  # Obtain the rows of the Golub et al. (1999) data set 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

## End(Not run)

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