Class EBAM

Description

This is a class representation for the Empirical Bayes Analysis of Microarrays (EBAM) proposed by Efron et al. (2001).

Seealso

ebam , find.a0 , FindA0-class

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

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.

Examples

# Load the data of Golub et al. (1999) contained in the package multtest.
data(golub)

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

# Perform an EBAM analysis for the two class unpaired case assuming
# unequal variances. Specify the fudge factor a0 by the suggested
# choice of find.a0
find.out <- find.a0(golub, golub.cl, rand = 123)
ebam.out <- ebam(find.out)
ebam.out

# Obtain the number of differentially
# expressed genes and the FDR if a gene is called differentially
# expressed if its posterior probability is larger than 0.8, 0.85,
# 0.9, 0.95.
print(ebam.out, c(0.8, 0.85, 0.9, 0.95))

# Generate a plot of the posterior probabilities for delta = 0.9.
plot(ebam.out, 0.9)

# Obtain the list of genes called differentially expressed if their
# posterior probability is larger than 0.99, and gene-specific
# statistics for these variables such as their z-value and their
# local FDR.
summary(ebam.out, 0.9)

Class FindA0

Description

This is a class representation for the specification of the fudge factor in an EBAM analysis as proposed by Efron et al. (2001).

Seealso

find.a0 , ebam , EBAM-class

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

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.

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).

Seealso

sam , args.sam , sam.plot2 , delta.plot

Note

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

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

Author

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. list("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: list("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. list("PNAS") , 98, 5116-5121.

Examples

# 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

EBAM Analysis for Categorical Data

Description

Generates the required statistics for an Empirical Bayes Analysis of Microarrays (EBAM) of categorical data such as SNP data.

Should not be called directly, but via ebam(..., method = chisq.ebam).

This function replaces cat.ebam .

Usage

chisq.ebam(data, cl, approx = NULL, B = 100, n.split = 1, 
   check.for.NN = FALSE, lev = NULL, B.more = 0.1, B.max = 50000,
   n.subset = 10, fast = FALSE, n.interval = NULL, df.ratio = 3,
   df.dens = NULL, knots.mode = NULL, type.nclass = "wand",
   rand = NA)

Arguments

Details

For each variable, Pearson's Chi-Square statistic is computed to test if the distribution of the variable differs between several groups. Since only one null distribution is estimated for all variables as proposed in the original EBAM application of Efron et al. (2001), all variables must have the same number of levels/categories.

Value

A list containing statistics required by ebam .

Seealso

EBAM-class , ebam , chisq.stat

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

Schwender, H. and Ickstadt, K. (2008). Empirical Bayes Analysis of Single Nucleotide Polymorphisms. BMC Bioinformatics , 9, 144.

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.

Examples

# Generate a random 1000 x 40 matrix consisting of the values
# 1, 2, and 3, and representing 1000 variables and 40 observations.

mat <- matrix(sample(3, 40000, TRUE), 1000)

# Assume that the first 20 observations are cases, and the
# remaining 20 are controls.

cl <- rep(1:2, e=20)

# Then an EBAM analysis for categorical data can be done by

out <- ebam(mat, cl, method=chisq.ebam, approx=TRUE)
out

# approx is set to TRUE to approximate the null distribution
# by the ChiSquare-distribution (usually, for such a small
# number of observations this might not be a good idea
# as the assumptions behind this approximation might not
# be fulfilled).

# The same results can also be obtained by employing
# contingency tables, i.e. by specifying data as a list.
# For this, we need to generate the tables summarizing
# groupwise how many observations show which level at
# which variable. These tables can be obtained by

library(scrime)
cases <- rowTables(mat[, cl==1])
controls <- rowTables(mat[, cl==2])
ltabs <- list(cases, controls)

# And the same EBAM analysis as above can then be
# performed by

out2 <- ebam(ltabs, method=chisq.ebam, approx=TRUE)
out2

SAM Analysis for Categorical Data

Description

Generates the required statistics for a Significance Analysis of Microarrays of categorical data such as SNP data.

Should not be called directly, but via sam(..., method = chisq.stat).

Replaces cat.stat

Usage

chisq.stat(data, cl, approx = NULL, B = 100, n.split = 1, 
   check.for.NN = FALSE, lev = NULL, B.more = 0.1, 
   B.max = 50000, n.subset = 10, rand = NA)

Arguments

Details

For each SNP (or more general, categorical variable), Pearson's Chi-Square statistic is computed to test if the distribution of the SNP differs between several groups. Since only one null distribution is estimated for all SNPs as proposed in the original SAM procedure of Tusher et al. (2001) all SNPs must have the same number of levels/categories.

Value

A list containing statistics required by sam .

Seealso

SAM-class , sam , chisq.ebam , trend.stat

Author

Holger Schwender, holger.schw@gmx.de

References

Schwender, H. (2005). Modifying Microarray Analysis Methods for Categorical Data -- SAM and PAM for SNPs. In Weihs, C. and Gaul, W. (eds.), Classification -- The Ubiquitous Challenge . Springer, Heidelberg, 370-377.

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

Examples

# Generate a random 1000 x 40 matrix consisting of the values
# 1, 2, and 3, and representing 1000 variables and 40 observations.

mat <- matrix(sample(3, 40000, TRUE), 1000)

# Assume that the first 20 observations are cases, and the
# remaining 20 are controls.

cl <- rep(1:2, e=20)

# Then an SAM analysis for categorical data can be done by

out <- sam(mat, cl, method=chisq.stat, approx=TRUE)
out

# approx is set to TRUE to approximate the null distribution
# by the ChiSquare-distribution (usually, for such a small
# number of observations this might not be a good idea
# as the assumptions behind this approximation might not
# be fulfilled).

# The same results can also be obtained by employing
# contingency tables, i.e. by specifying data as a list.
# For this, we need to generate the tables summarizing
# groupwise how many observations show which level at
# which variable. These tables can be obtained by

library(scrime)
cases <- rowTables(mat[, cl==1])
controls <- rowTables(mat[, cl==2])
ltabs <- list(cases, controls)

# And the same SAM analysis as above can then be
# performed by

out2 <- sam(ltabs, method=chisq.stat, approx=TRUE)
out2

Delta Plots

Description

Generates both a plot of $Delta$ vs. the FDR and a plot of $Delta$ vs. the number of identified genes in a SAM analysis.

Usage

delta.plot(object, delta = NULL, helplines = FALSE)

Arguments

Details

The $Delta$ plots are a visualization of the table generated by sam that contains the estimated FDR and the number of identified genes for a set of $Delta$ values.

Value

Two plots in one graphsheet: The plot of $Delta$ vs. FDR and the plot of $Delta$ vs. the number of identified genes.

Seealso

SAM-class , sam

Author

Holger Schwender, holger.schw@gmx.de

References

Tusher, V., Tibshirani, R., and Chu, G. (2001). Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. PNAS , 98, 5116-5121.

Examples

# Load the package multtest and the data of Golub et al. (1999)
# contained in multtest.
library(multtest)
data(golub)

# Perform a SAM analysis.
sam.out<-sam(golub, golub.cl, B=100, rand=123)

# Generate the Delta plots for the default set of Deltas computed by sam.
delta.plot(sam.out)

# Another way of generating the same plot.
plot(sam.out)

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

Density Estimation

Description

Estimates the density of a vector of observations by a Poisson regression fit to histogram counts.

Usage

denspr(x, n.interval = NULL, df = 5, knots.mode = TRUE, 
      type.nclass = c("wand", "scott", "FD"), addx=FALSE)

Arguments

Value

An object of class denspr consisting of

*

Seealso

cat.ebam

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., and Tibshirani, R. (1996). Using specially designed exponential families for density estimation. Annals of Statistics , 24, 2431--2461.

Wand, M.P. (1997). Data-based choice of histogram bin width. American Statistician , 51, 59--64.

Examples

# Generating some random data.
x <- rnorm(10000)
out <- denspr(x, addx=TRUE)
plot(out)

# Or for an asymmetric density.
x <- rchisq(10000, 2)
out <- denspr(x, df=3, addx=TRUE)
plot(out)

SAM Analysis Using a Modified t-statistic

Description

Computes the required statistics for a Significance Analysis of Microarrays (SAM) using either a (modified) t- or F-statistic.

Should not be called directly, but via the function sam.

Usage

d.stat(data, cl, var.equal = FALSE, B = 100, med = FALSE, s0 = NA, 
      s.alpha = seq(0, 1, 0.05), include.zero = TRUE, n.subset = 10, 
      mat.samp = NULL, B.more = 0.1, B.max = 30000, gene.names = NULL,
      R.fold = 1, use.dm = TRUE, R.unlog = TRUE, na.replace = TRUE, 
      na.method = "mean", rand = NA)

Arguments

Value

An object of class SAM.

Seealso

SAM-class , sam , z.ebam

Author

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.

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

Empirical Bayes Analysis of Microarrays

Description

Performs an Empirical Bayes Analysis of Microarrays (EBAM). 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 EBAM procedure for categorical data such as SNP data and the possibility to employ an user-written score function.

Usage

ebam(x, cl, method = z.ebam, delta = 0.9, which.a0 = NULL, 
       control = ebamControl(), gene.names = dimnames(x)[[1]],
       ...)

Arguments

Value

An object of class EBAM.

Seealso

EBAM-class , find.a0 , z.ebam , wilc.ebam , chisq.ebam

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. JASA , 96, 1151-1160.

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

Storey, J.D. and Tibshirani, R. (2003). Statistical Significance for Genome-Wide Studies. Proceedings of the National Academy of Sciences , 100, 9440-9445.

Examples

# Load the data of Golub et al. (1999) contained in the package multtest.
data(golub)

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

# Perform an EBAM analysis for the two class unpaired case assuming
# unequal variances. Specify the fudge factor a0 by the suggested
# choice of find.a0
find.out <- find.a0(golub, golub.cl, rand = 123)
ebam.out <- ebam(find.out)
ebam.out

# Since a0 = 0 leads to the largest number of genes (i.e. the suggested
# choice of a0), the following leads to the same results as the above
# analysis (but only if the random number generator, i.e. rand, is set
# to the same number).
ebam.out2 <- ebam(golub, golub.cl, a0 = 0, fast = TRUE, rand = 123)
ebam.out2

# If fast is set to TRUE in ebam, a crude estimate of the number of
# falsely called genes is used (see the help file for z.ebam). This
# estimate is always employed in find.a0.
# The exact number is used in ebam when performing
ebam.out3 <- ebam(golub, golub.cl, a0 = 0, rand = 123)
ebam.out3

# Since this is the recommended way, we use ebam.out3 at the end of
# the Examples section for further analyses.



# Perform an EBAM analysis for the two class unpaired case assuming
# equal group variances. Set a0 = 0, and use B = 50 permutations
# of the class labels.
ebam.out4 <- ebam(golub, golub.cl, a0 = 0, var.equal = TRUE, B = 50,
rand = 123)
ebam.out4

# Perform an EBAM analysis for the two class unpaired cased assuming
# unequal group variances. Use the median (i.e. the 50% quantile)
# of the standard deviations of the genes as fudge factor a0. And
# obtain the number of genes and the FDR if a gene is called
# differentially when its posterior probability is larger than
# 0.95.
ebam.out5 <- ebam(golub, golub.cl, quan.a0 = 0.5, delta = 0.95,
rand = 123)
ebam.out5

# For the third analysis, obtain the number of differentially
# expressed genes and the FDR if a gene is called differentially
# expressed if its posterior probability is larger than 0.8, 0.85,
# 0.9, 0.95.
print(ebam.out3, c(0.8, 0.85, 0.9, 0.95))

# Generate a plot of the posterior probabilities for delta = 0.9.
plot(ebam.out3, 0.9)

# Obtain the list of genes called differentially expressed if their
# posterior probability is larger than 0.99, and gene-specific
# statistics for these variables such as their z-value and their
# local FDR.
summary(ebam.out3, 0.99)

Further EBAM Arguments

Description

Specifies most of the optional arguments of ebam and find.a0 .

Usage

ebamControl(p0 = NA, p0.estimation = c("splines", "interval", "adhoc"), 
   lambda = NULL, ncs.value = "max", use.weights = FALSE)
   
find.a0Control(p0.estimation = c("splines", "adhoc", "interval"), 
   lambda = NULL, ncs.value = "max", use.weights = FALSE,
   n.chunk = 5, n.interval = 139, df.ratio = NULL)

Arguments

Details

These parameters should only be changed if they are fully understood.

Value

A list containing the values of the parameters that are used in ebam or find.a0 , respectively.

Seealso

limma2ebam , ebam , find.a0

Author

Holger Schwender, holger.schwender@udo.edu

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. JASA , 96, 1151-1160.

Storey, J.D. and Tibshirani, R. (2003). Statistical Significance for Genome-Wide Studies. Proceedings of the National Academy of Sciences , 100, 9440-9445.

Finding the Threshold Delta

Description

Computes the value of the threshold Delta for a given FDR or number of genes/variables in a SAM or EBAM analysis.

Usage

findDelta(object, fdr = NULL, genes = NULL, prec = 6, initial = NULL,
      verbose = FALSE)

Arguments

Value

If a value of Delta is found for the exact value of fdr or genes , then a vector of length 3 consisting of Delta and the corresponding number of genes and the estimated FDR. If such a value is not found, then a matrix with two rows and three columns, where the two rows contain the number of genes/variables and the estimated FDR for the two considered values of Delta that provide the closest upper and lower bounds to the desired FDR (if fdr is specified) or number of genes/variables (if genes is specified.)

Seealso

sam , ebam

Author

Holger Schwender, holger.schwender@udo.edu

Computation of the Fudge Factor

Description

Suggests an optimal value for the fudge factor in an EBAM analysis as proposed by Efron et al. (2001).

Usage

find.a0(data, cl, method = z.find, B = 100, delta = 0.9, 
      quan.a0 = (0:5)/5, include.zero = TRUE, 
      control = find.a0Control(), gene.names = dimnames(data)[[1]],
      rand = NA, ...)

Arguments

Details

The suggested choice for the fudge factor is the value of $a_0$ that leads to the largest number of genes showing a posterior probability larger than delta .

Actually, only the genes having a posterior probability larger than delta are called differentially expressed that do not exhibit a test score less extreme than the score of a gene whose posterior probability is less than delta . So, let's say, we have done an EBAM analysis with a t-test and we have ordered the genes by their t-statistic. Let's further assume that Gene 1 to Gene 5 (i.e. the five genes with the lowest t-statistics), Gene 7 and 8, Gene 3012 to 3020, and Gene 3040 to 3051 are the only genes that show a posterior probability larger than delta . Then, Gene 1 to 5, and 3040 to 3051 are called differentially expressed, but Gene 7 and 8, and 3012 to 3020 are not called differentially expressed, since Gene 6 and Gene 3021 to 3039 show a posterior probability less than delta .

Value

An object of class FindA0.

Seealso

ebam , FindA0-class , find.a0Control

Note

The numbers of differentially expressed genes can differ between find.a0 and ebam , even though the same value of the fudge factor is used, since in find.a0 the observed and permuted test scores are monotonically transformed such that the observed scores follow a standard normal distribution (if the test statistic can take both positive and negative values) and an F-distribution (if the test statistic can only take positive values) for each possible choice of the fudge factor.

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

Examples

# Load the data of Golub et al. (1999) contained in the package multtest.
data(golub)

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

# Obtain the number of differentially expressed genes and the FDR for the
# default set of values for the fudge factor.
find.out <- find.a0(golub, golub.cl, rand = 123)
find.out

# Obtain the number of differentially expressed genes and the FDR when using
# the t-statistic assuming equal group variances
find.out2 <- find.a0(golub, golub.cl, var.equal = TRUE, rand = 123)

# Using the Output of the first analysis with find.a0, the number of
# differentially expressed genes and the FDR for other values of
# delta, e.g., 0.95, can be obtained by
print(find.out, 0.95)

# The logit-transformed posterior probabilities can be plotted by
plot(find.out)

# To avoid the logit-transformation, set logit = FALSE.
plot(find.out, logit = FALSE)

Fudge Factor

Description

Computes the fudge factor as described by Tusher et al. (2001).

Usage

fudge2(r, s, alpha = seq(0, 1, 0.05), include.zero = TRUE)

Arguments

Value

*

Seealso

SAM-class , sam

Author

Holger Schwender, holger.schw@gmx.de

References

Tusher, V., Tibshirani, R., and Chu, G. (2001). Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. PNAS , 98, 5116-5121.

EBAM and SAM for Fuzzy Genotype Calls

Description

Computes the required statistics for an Empirical Bayes Analysis of Microarrays (EBAM; Efron et al., 2001) or a Significant Analysis of Microarrays (SAM; Tusher et al., 2001), respectively, based on the score statistic proposed by Louis et al. (2010) for fuzzy genotype calls or approximate Bayes Factors (Wakefield, 2007) determined using this score statistic.

Should not be called directly, but via ebam(..., method = fuzzy.ebam) or sam(..., method = fuzzy.stat) , respectively.

Usage

fuzzy.ebam(data, cl, type = c("asymptotic", "permutation", "abf"), W = NULL, 
    logbase = exp(1), addOne = TRUE, df.ratio = NULL, n.interval = NULL, 
    df.dens = 5, knots.mode = TRUE, type.nclass = c("FD", "wand", "scott"), 
    fast = FALSE, B = 100, B.more = 0.1, B.max = 30000, n.subset = 10, rand = NA)
    
fuzzy.stat(data, cl, type = c("asymptotic", "permutation", "abf"), W = NULL, 
    logbase = exp(1), addOne = TRUE, B = 100, B.more = 0.1, B.max = 30000, 
    n.subset = 10, rand = NA)

Arguments

Value

A list containing statistics required by ebam or sam .

Seealso

ebam , sam , EBAM-class , SAM-class

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

Louis, T.A., Carvalho, B.S., Fallin, M.D., Irizarry, R.A., Li, Q., and Ruczinski, I. (2010). Association Tests that Accommodate Genotyping Errors. In Bernardo, J.M., Bayarri, M.J., Berger, J.O., Dawid, A.P., Heckerman, D., Smith, A.F.M., and West, M. (eds.), Bayesian Statistics 9 , 393-420. Oxford University Press, Oxford, UK. With Discussion.

Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. PNAS , 98, 5116-5121.

Wakefield, J. (2007). A Bayesian Measure of Probability of False Discovery in Genetic Epidemiology Studies. AJHG , 81, 208-227.

Help files or argument list for EBAM-specific methods

Description

Displays the help page or the argument list, respectively, for a EBAM-specific method.

Usage

help.ebam(method)
   args.ebam(method)

Arguments

Value

The arguments of the specified method are displayed or a html page containing the help for the specified method is opened, respectively.

Seealso

EBAM-class , ebam

Author

Holger Schwender, holger.schw@gmx.de

Examples

# Displays the arguments of the function summary
args.ebam(summary)

# Opens the help page in the browser
help.ebam(summary)

Help files or argument list for FindA0-specific methods

Description

Displays the help page or the argument list, respectively, for a FindA0-specific method.

Usage

help.finda0(method)
   args.finda0(method)

Arguments

Value

The arguments of the specified method are displayed or a html page containing the help for the specified method is opened, respectively.

Seealso

FindA0-class , find.a0

Author

Holger Schwender, holger.schw@gmx.de

Examples

# Displays the arguments of the function summary
args.finda0(summary)

# Opens the help page in the browser
help.finda0(summary)

Help files or argument list for SAM-specific methods

Description

Displays the help page or the argument list, respectively, for a SAM-specific method.

Usage

help.sam(method)
   args.sam(method)

Arguments

Value

The arguments of the specified method are displayed or a html page containing the help for the specified method is opened, respectively.

Seealso

SAM-class , sam

Author

Holger Schwender, holger.schw@gmx.de

Examples

# Displays the arguments of the function summary
args.sam(summary)

# Opens the help page in the browser
help.sam(summary)

limma to SAM or EBAM

Description

Transforms the output of an analysis with limma into a SAM or EBAM object, such that a SAM or EBAM analysis, respectively, can be performed using the test statistics provided by limma .

Usage

limma2sam(fit, coef, moderate = TRUE, sam.control = samControl())
limma2ebam(fit, coef, moderate = TRUE, delta = 0.9, 
   ebam.control = ebamControl())

Arguments

Value

An object of class SAM or EBAM .

Seealso

sam , ebam , SAM-class , EBAM-class , samControl , ebamControl

Author

Holger Schwender, holger.schwender@udo.edu

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. list("JASA") , 96, 1151-1160.

Smyth, G.K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. list("Statistical Applications in Genetics and ", " Molecular Biology") , 3(1), Article 3.

Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. list("PNAS") , 98, 5116-5121.

Links for a list of genes

Description

Generates a htmlpage with links to several public repositories for a list of genes.

Usage

link.genes(genenames, filename, entrez = TRUE, refseq = TRUE, symbol = TRUE,
    omim = FALSE, ug = FALSE, fullname = FALSE, which.refseq = "NM",
    chipname = "", cdfname = NULL, refsnp = NULL, max.associated = 2,
    dataframe = NULL, title = NULL, bg.col = "white", text.col = "black", 
    link.col = "blue", tableborder = 1, new.window = TRUE, load = TRUE)

Arguments

Seealso

SAM-class , sam , link.siggenes , sam2html

Author

Holger Schwender, holger.schw@gmx.de

Links for a SAM or an EBAM object

Description

Generates a html page with links to several public repositories for a list of genes called differentially expressed when using a specific Delta value in a SAM or an EBAM analysis.

Usage

link.siggenes(object, delta, filename, gene.names = NULL, addDataFrame = TRUE,
        entrez = TRUE, refseq = TRUE, symbol = TRUE, omim = FALSE, ug = FALSE,
        fullname = FALSE, which.refseq = "NM", chipname = "", cdfname = NULL, 
        refsnp = NULL, max.associated = 2, n.digits = 3, title = NULL, 
        bg.col = "white", text.col = "black", link.col = "blue", tableborder = 1, 
        new.window = TRUE, load = TRUE)

Arguments

Seealso

sam , ebam , link.genes , sam2html , ebam2html

Author

Holger Schwender, holger.schw@gmx.de

List of the significant genes

Description

Lists the genes called differentially expressed by the SAM or the EBAM analysis for a specified value of the threshold $Delta$ .

Usage

list.siggenes(object, delta, file = "", gene.names = NULL, order = TRUE, 
  text = NULL, append = FALSE)

Arguments

Value

A list of significant genes either shown in the console or stored in a file.

Seealso

sam , ebam

Author

Holger Schwender, holger.schw@gmx.de

Examples

# Load the package multtest and the data of Golub et al. (1999)
# contained in pkg{multtest}.
library(multtest)
data(golub)

# Perform a SAM analysis.
sam.out<-sam(golub, golub.cl, B=100, rand=123)

# List the genes called significant by SAM using Delta = 3.1.
list.siggenes(sam.out, 3.1, gene.names=golub.gnames[,2])

MD Plot

Description

Generates an MD plot for a specified value of Delta.

Contrary to a SAM plot in which the observed values of the test statistic $D$ are plotted against the expected ones, the difference $M$ between the observed and the expected values are plotted against the observed values in an MD plot.

Usage

md.plot(object, delta, pos.stats = 1, sig.col = 3, xlim = NULL, ylim = NULL, 
        main = NULL, xlab = NULL, ylab = NULL, xsym = NULL, ysym = NULL, 
        forceDelta = FALSE, includeZero = TRUE, lab = c(10, 10, 7), pch = NULL, 
        sig.cex = 1, ...)

Arguments

Value

A MD plot.

Seealso

sam , sam.plot2

Author

Holger Schwender, holger.schw@gmx.de

Examples

# 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)

# Generate a SAM plot for Delta = 2
plot(sam.out, 2)

# As an alternative, the MD plot can be generated.
md.plot(sam.out, 2)

Number of cells in a histogram

Description

Computes the number of cells in a histogram using the method of Wand (1994).

Usage

nclass.wand(x, level = 1)

Arguments

Details

nclass.wand calls dpih , and then computes the number of cells corresponding to the optimal bin width returned by dpih .

Value

A numeric value specifying the number of cells for the histogram of x .

Seealso

denspr

References

Wand, M.P. (1997). Data-based choice of histogram bin width. American Statistician , 51, 59--64.

Estimation of the prior probability

Description

Estimates the prior probability that a gene is not differentially expressed by the natural cubic splines based method of Storey and Tibshirani (2003).

Usage

pi0.est(p, lambda = seq(0, 0.95, 0.05), ncs.value = "max", 
      ncs.weights = NULL)

Arguments

Details

For each value of lambda , $pi_0(lambda)$ is computed by the number of p-values p larger than $lambda$ divided by $(1-lambda)/m$ , where $m$ is the length of p .

If lambda is a value, $pi_0(lambda)$ is the estimate for the prior probabiltity $pi_0$ that a gene is not differentially expressed.

If lambda is a vector, a natural cubic spline $h$ with 3 degrees of freedom is fitted through the data points $(lambda,pi_0(lambda))$ , where each point is weighed by ncs.weights . $pi_0$ is estimated by $h(v)$ , where $v=max{lambda}$ if ncs.value="max" , and $v=1$ if ncs.value="paper" .

Value

*

Seealso

SAM-class , sam , qvalue.cal

Author

Holger Schwender, holger.schw@gmx.de

References

Storey, J.D., and Tibshirani, R. (2003). Statistical Significance for Genome-wide Studies. PNAS , 100, 9440-9445.

Examples

# Load the package multtest and the data of Golub et al. (1999)
# contained in multtest.
library(multtest)
data(golub)

# Perform a SAM analysis.
sam.out<-sam(golub, golub.cl, B=100, rand=123)

# Estimate the prior probability that a gene is not significant
pi0.est(sam.out@p.value)

Plot Arguments

Description

Utility function for generating a plot of a SAM or an EBAM object in an html output.

Usage

plotArguments(pos.stats = NULL, sig.col = 3, xlim = NULL, ylim = NULL,
        main = NULL, xlab = NULL, ylab = NULL, pty = "s", lab = c(10, 10, 7),
        pch = NULL, sig.cex = 1, stats.cex = 0.8, y.intersp = 1.3)

Arguments

Value

A list required by sam2html or ebam2html if addPlot = TRUE .

Seealso

sam2html , ebam2html

Author

Holger Schwender, holger.schw@gmx.de

Plot Arguments

Description

Utility function for generating a plot of the posterior probabilities in an html file when searching for the optimal value of the fudge factor in an EBAM analysis.

Usage

plotFindArguments(onlyTab = FALSE, logit = TRUE, pos.legend = NULL,
        legend.cex = 0.8, col = NULL, main = NULL, xlab = NULL, ylab = NULL,
        only.a0 = FALSE, lty = 1, lwd = 1, y.intersp = 1.1)

Arguments

Value

A list required by ebam2html if findA0 is specified.

Seealso

ebam2html

Author

Holger Schwender, holger.schw@gmx.de

Computation of the q-value

Description

Computes the q-values of a given set of p-values.

Usage

qvalue.cal(p, p0, version = 1)

Arguments

Details

Using version = 1 in qvalue.cal corresponds to setting robust = FALSE in the function qvalue of John Storey's list() package list("qvalue") , while version = 2 corresponds to robust = TRUE .

Value

A vector of the same length as p containing the q-values corresponding to the p-values in p .

Seealso

pi0.est , SAM-class , sam

Author

Holger Schwender, holger.schw@gmx.de

References

Storey, J.D. (2003). The positive False Discovery Rate: A Bayesian Interpretation and the q-value. Annals of Statistics , 31, 2013-2035.

Storey, J.D., and Tibshirani, R. (2003). Statistical Significance for Genome-wide Studies. PNAS , 100, 9440-9445.

Examples

# Load the package multtest and the data of Golub et al. (1999)
# contained in multtest.
library(multtest)
data(golub)

# Perform a SAM analysis.
sam.out<-sam(golub, golub.cl, B=100, rand=123)

# Estimate the prior probability that a gene is not significant.
pi0 <- pi0.est(sam.out@p.value)$p0

# Compute the q-values of the genes.
q.value <- qvalue.cal(sam.out@p.value, pi0)

Rowwise Wilcoxon Rank Sum Statistics

Description

Computes either the Wilcoxon Rank Sum or Signed Rank Statistics for all rows of a matrix simultaneously.

Usage

rowWilcoxon(X, cl, rand = NA)

Arguments

Details

If there are ties, then the ranks of the observations belonging to the same group of tied observations will be set to the maximum rank available for the corresponding group.

Value

A numeric vector containing Wilcoxon rank statistics for each row of X .

Seealso

wilc.stat , wilc.ebam

Author

Holger Schwender, holger.schw@gmx.de

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

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: list(" ", " ", list(list(list("d"), ":"), list("a numeric vector consisting of the expression scores of the genes.")), " ", " ", list(list(list("d.bar"), ":"), list("a numeric vector of the same length as ", list("na.exclude(d)"), " specifying ", " the expected expression scores under the null hypothesis.")), " ", " ", list(list(list("p.value"), ":"), list("a numeric vector of the same length as ", list("d"), " containing ", " the raw, unadjusted p-values of the genes.")),

"

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

list(list(list("s"), ":"), list("a numeric vector of the same length as ", list("d"), " containing the standard deviations 

", " of the genes. If no standard deviation can be calculated, set ", list("s = numeric(0)"), ".")), " ", " ", list(list(list("s0"), ":"), list("a numeric value specifying the fudge factor. If no fudge factor is calculated, ", " set ", list("s0 = numeric(0)"), ".")), " ", " ", list(list(list("mat.samp"), ":"), list("a matrix with B rows and ", list(

    "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 ", list("mat.samp = matrix(numeric(0))"), ".")), " ", " ", list(list(list("msg"), ":"), list("a character string or vector containing information about, e.g., which type of analysis ", " has been performed. ", list("msg"), " is printed when the function ", list("print"), " or ",

    "        ", list("summary"), ", respectively, is called. If no such message should be printed, set ", list("msg = """), ".")), "

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

Seealso

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

Author

Holger Schwender, holger.schw@gmx.de

References

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

Schwender, H. (2004). Modifying Microarray Analysis Methods for Categorical Data -- SAM and PAM for SNPs. To appear in: list("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. list("PNAS") , 98, 5116-5121.

Examples

# 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)

Further SAM Arguments

Description

Specifies most of the optional arguments of sam .

Usage

samControl(delta = NULL, n.delta = 10, p0 = NA, lambda = seq(0, 0.95, 0.05), 
   ncs.value = "max", ncs.weights = NULL, q.version = 1)

Arguments

Details

These parameters should only be changed if they are fully understood.

Value

A list containing the values of the parameters that are used in sam .

Seealso

limma2sam , sam

Author

Holger Schwender, holger.schwender@udo.edu

References

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

Storey, J.D. and Tibshirani, R. (2003). Statistical Significance for Genome-Wide Studies. Proceedings of the National Academy of Sciences , 100, 9440-9445.

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

SAM Plot

Description

Generates a SAM plot for a specified value of Delta.

Usage

sam.plot2(object, delta, pos.stats = NULL, sig.col = 3, xlim = NULL, 
        ylim = NULL, main = NULL, xlab = NULL, ylab = NULL, pty = "s", 
        lab = c(10, 10, 7), pch = NULL, sig.cex = 1, ...)

Arguments

Value

A SAM plot.

Seealso

SAM-class , sam , md.plot

Author

Holger Schwender, holger.schw@gmx.de

References

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

Examples

# 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)

# Generate a SAM plot for Delta = 2
sam.plot2(sam.out, 2)

# Alternatively way of generating the same SAM plot
plot(sam.out, 2)

# As an alternative, the MD plot can be generated.
md.plot(sam.out, 2)

CSV file of a SAM or an EBAM object

Description

Generates a csv file for either a SAM or an EBAM object for the use in Excel. This csv file can contain general information as the number of differentially expressed genes and the estimated FDR, and gene-specific information on the differentially expressed genes.

Usage

sam2excel(object, delta, file, excel.version=1, n.digits = 3, what = "both", 
        entrez = FALSE, chip = "", quote = FALSE)
        
 ebam2excel(object, delta, file, excel.version=1, n.digits = 4, what = "both", 
        entrez = FALSE, chip = "", quote = FALSE)

Arguments

Seealso

sam , sam2html , ebam , ebam2html

Author

Holger Schwender, holger.schw@gmx.de

HTML page for a SAM or an EBAM object

Description

Generates a html page for a SAM or an EBAM object. This html page can contain general information as the number of differentially expressed genes and the estimated FDR, the SAM or EBAM plot, and gene-specific information on the differentially expressed genes.

Usage

ebam2html(object, delta, filename, addStats = TRUE, addPlot = TRUE, 
        addGenes = TRUE, findA0 = NULL, varName = NULL, entrez = TRUE, 
        refseq = TRUE, symbol = TRUE, omim = FALSE, ug = FALSE,
        fullname = FALSE, chipname = "", cdfname = NULL, 
        which.refseq = "NM", refsnp = NULL, max.associated = 2,
        n.digits = 3, bg.col = "white", text.col = "black", link.col = "blue", 
        plotArgs = plotArguments(), plotFindArgs = plotFindArguments(), 
        bg.plot.adjust = FALSE, plotname = NULL, plotborder = 0, 
        tableborder = 1, new.window = TRUE, load = TRUE, ...)
 sam2html(object, delta, filename, addStats = TRUE, addPlot = TRUE, 
        addGenes = TRUE, varName = NULL, entrez = TRUE, refseq = TRUE, 
        symbol = TRUE, omim = FALSE, ug = FALSE, fullname = FALSE, 
        bonf = FALSE, chipname = "", cdfname = NULL, which.refseq = "NM", 
        refsnp = NULL, max.associated = 2, n.digits = 3, bg.col = "white", 
        text.col = "black", link.col = "blue", plotArgs = plotArguments(), 
        bg.plot.adjust = FALSE, plotname = NULL, plotborder = 0, 
        tableborder = 1, new.window = TRUE, load = TRUE, ...)

Arguments

Seealso

SAM-class , sam , EBAM-class , ebam , link.genes , link.siggenes , plotArguments , plotFindArguments

Author

Holger Schwender, holger.schw@gmx.de

Internal siggenes functions

Description

Internal siggenes functions.

Details

These functions are not meant to be directly called by the user.

Author

Holger Schwender, holger.schw@gmx.de

Classes sumSAM and sumEBAM

Description

These classes are just used for a nicer output of the summary of an object of class SAM or EBAM, respectively.

Seealso

SAM-class , EBAM-class

Author

Holger Schwender, holger.schw@gmx.de

EBAM Analysis of Linear Trend

Description

Generates the required statistics for an Empirical Bayes Analysis of Microarrays for a linear trend in (ordinal) data.

In the two-class case, the Cochran-Armitage trend statistic is computed. Otherwise, the statistic for the general test of trend described on page 87 of Agresti (2002) is determined.

Should not be called directly, but via ebam(..., method = trend.ebam).

Usage

list(list("trend.ebam"), list("default"))(data, cl, catt = TRUE, approx = TRUE, n.interval = NULL,
    df.dens = NULL, knots.mode = NULL, type.nclass = "wand",
    B = 100, B.more = 0.1, B.max = 50000, n.subset = 10, 
    fast = FALSE, df.ratio = 3, rand = NA, ...)
    
list(list("trend.ebam"), list("list"))(data, cl, catt = TRUE, approx = TRUE, n.interval = NULL, 
    df.dens = NULL, knots.mode = NULL, type.nclass = "wand", ...)

Arguments

Value

A list containing statistics required by ebam .

Seealso

EBAM-class , ebam , trend.stat , chisq.ebam

Author

Holger Schwender, holger.schw@gmx.de

References

Agresti, A. (2002). Categorical Data Analysis . Wiley, Hoboken, NJ. 2nd Edition.

Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

Examples

# Generate a random 1000 x 40 matrix consisting of the values
# 1, 2, and 3, and representing 1000 variables and 40 observations.

mat <- matrix(sample(3, 40000, TRUE), 1000)

# Assume that the first 20 observations are cases, and the
# remaining 20 are controls, and that the values 1, 2, 3 in
# mat can be interpreted as scores for the different levels
# of the variables.

cl <- rep(1:2, e=20)

# Then an EBAM analysis of linear trend can be done by

out <- ebam(mat, cl, method=trend.ebam)
out

# The same results can also be obtained by employing
# contingency tables, i.e. by specifying data as a list.
# For this, we need to generate the tables summarizing
# groupwise how many observations show which level at
# which variable. These tables can be obtained by

library(scrime)
cases <- rowTables(mat[, cl==1])
controls <- rowTables(mat[, cl==2])
ltabs <- list(cases, controls)

# And the same EBAM analysis as above can then be
# performed by

out2 <- ebam(ltabs, method=trend.ebam)
out2

SAM Analysis of Linear Trend

Description

Generates the required statistics for a Significance Analysis of Microarrays for a linear trend in (ordinal) data.

In the two-class case, the Cochran-Armitage trend statistic is computed. Otherwise, the statistic for the general test of trend described on page 87 of Agresti (2002) is determined.

Should not be called directly, but via sam(..., method = trend.stat).

Usage

list(list("trend.stat"), list("default"))(data, cl, catt = TRUE, approx = TRUE, B = 100, 
   B.more = 0.1, B.max = 50000, n.subset = 10, rand = NA, ...)
   
list(list("trend.stat"), list("list"))(data, cl, catt = TRUE, approx = TRUE, B = 100, 
   B.more = 0.1, B.max = 50000, n.subset = 10, rand = NA, ...)

Arguments

Value

A list containing statistics required by sam .

Seealso

SAM-class , sam , chisq.stat , trend.ebam

Author

Holger Schwender, holger.schw@gmx.de

References

Agresti, A. (2002). Categorical Data Analysis . Wiley, Hoboken, NJ. 2nd Edition.

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

Examples

# Generate a random 1000 x 40 matrix consisting of the values
# 1, 2, and 3, and representing 1000 variables and 40 observations.

mat <- matrix(sample(3, 40000, TRUE), 1000)

# Assume that the first 20 observations are cases, and the
# remaining 20 are controls, and that the values 1, 2, 3 in mat
# can be interpreted as scores for the different levels
# of the variables represented by the rows of mat.

cl <- rep(1:2, e=20)

# Then an SAM analysis of linear trend can be done by

out <- sam(mat, cl, method=trend.stat)
out

# The same results can also be obtained by employing
# contingency tables, i.e. by specifying data as a list.
# For this, we need to generate the tables summarizing
# groupwise how many observations show which level at
# which variable. These tables can be obtained by

library(scrime)
cases <- rowTables(mat[, cl==1])
controls <- rowTables(mat[, cl==2])
ltabs <- list(cases, controls)

# And the same SAM analysis as above can then be
# performed by

out2 <- sam(ltabs, method=trend.stat, approx=TRUE)
out2

EBAM Analysis Using Wilcoxon Rank Statistics

Description

Generates the required statistics for an Empirical Bayes Analysis of Microarrays analysis using standardized Wilcoxon rank statistics.

Should not be called directly, but via ebam(..., method = wilc.ebam).

Usage

wilc.ebam(data, cl, approx50 = TRUE, ties.method = c("min", "random", 
       "max"), use.offset = TRUE, df.glm = 5, use.row = FALSE, rand = NA)

Arguments

Details

Standardized versions of the Wilcoxon rank statistics are computed. This means that $W* = (W - W{mean}) / W{sd}$ is used as expression score $z$ , where $W$ is the usual Wilcoxon rank sum statistic or Wilcoxon signed rank statistic, respectively.

In the computation of these statistics, the ranks of ties are by default set to the minimum rank. In the computation of the Wilcoxon signed rank statistic, zeros are randomly set either to a very small positive or negative value.

If there are less than 50 observations in each of the groups, the exact null distribution will be used. If there are more than 50 observations in at least one group, the null distribution will by default be approximated by the standard normal distribution. It is, however, still possible to compute the exact null distribution by setting approx50 to FALSE .

Value

A list of statistics required by ebam .

Seealso

ebam , wilc.stat

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Storey, J.D., Tibshirani, R. (2001). Microarrays, empirical Bayes methods, and the false discovery rate, Technical Report , Department of Statistics, Stanford University.

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.

SAM Analysis Using Wilcoxon Rank Statistics

Description

Generates the required statistics for a Significance Analysis of Microarrays analysis using standardized Wilcoxon rank statistics.

Should not be called directly, but via sam(..., method = wilc.stat).

Usage

wilc.stat(data, cl, gene.names = NULL, R.fold = 1, use.dm = FALSE,
       R.unlog = TRUE, na.replace = TRUE, na.method = "mean", 
       approx50 = TRUE, ties.method=c("min","random","max"), 
       use.row = FALSE, rand = NA)

Arguments

Details

Standardized versions of the Wilcoxon rank statistics are computed. This means that $W* = (W - W{mean}) / W{sd}$ is used as expression score $d$ , where $W$ is the usual Wilcoxon rank sum statistic or Wilcoxon signed rank statistic, respectively.

In the computation of these statistics, the ranks of ties are by default set to the minimum rank. In the computation of the Wilcoxon signed rank statistic, zeros are randomly set either to a very small positive or negative value.

If there are less than 50 observations in each of the groups, the exact null distribution will be used. If there are more than 50 observations in at least one group, the null distribution will by default be approximated by the standard normal distribution. It is, however, still possible to compute the exact null distribution by setting approx50 to FALSE .

Value

A list containing statistics required by sam .

Seealso

SAM-class , sam , wilc.ebam

Author

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.

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

EBAM analysis Using t- or F-test

Description

Computes the required statistics for an Empirical Bayes Analysis with a modified t- or F-test.

Should not be called directly, but via ebam(..., method = z.ebam) or find.a0(..., method = z.find) , respectively.

Usage

z.ebam(data, cl, a0 = NULL, quan.a0 = NULL, B = 100, var.equal = FALSE, 
      B.more = 0.1, B.max = 30000, n.subset = 10, fast = FALSE, 
      n.interval = 139, df.ratio = NULL, rand = NA)
  
  z.find(data, cl, B = 100, var.equal = FALSE, B.more = 0.1, 
      B.max = 30000)

Arguments

Value

A list of object required by find.a0 or ebam , respectively.

Seealso

ebam , find.a0 , d.stat

Author

Holger Schwender, holger.schw@gmx.de

References

Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment, JASA , 96, 1151-1160.

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.