Description Objects from the Class Slots Methods Note Author(s) References See Also Examples

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

Objects can be created using the functions `sam`

, `sam.dstat`

,
`sam.wilc`

and `sam.snp`

.

`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`""`

.

- 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)`

.`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)`

.

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

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

Holger Schwender, [email protected]

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.

`sam`

,`args.sam`

,`sam.plot2`

,
`delta.plot`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ```
## 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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