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

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## Default S3 method:
trend.ebam(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, ...)
    
## S3 method for class 'list'
trend.ebam(data, cl, catt = TRUE, approx = TRUE, n.interval = NULL, 
    df.dens = NULL, knots.mode = NULL, type.nclass = "wand", ...)

Arguments

data

either a numeric matrix or data frame, or a list. If a matrix or data frame, then each row must correspond to a variable (e.g., a SNP), and each column to a sample (i.e.\ an observation). The values in the matrix or data frame are interpreted as the scores for the different levels of the variables.

If the number of observations is huge it is better to specify data as a list consisting of matrices, where each matrix represents one group and summarizes how many observations in this group show which level at which variable. The row and column names of all matrices must be identical and in the same order. The column names must be interpretable as numeric scores for the different levels of the variables. These matrices can, e.g., be generated using the function rowTables from the package scrime. (It is recommended to use this function, as trend.stat has been made for using the output of rowTables.) For details on how to specify this list, see the examples section on this man page, and the help for rowChisqMultiClass in the package scrime.

cl

a numeric vector of length ncol(data) indicating to which classes the samples in the matrix or data frame data belongs. The values in cl must be interpretable as scores for the different classes. Must be specified if data is a matrix or a data frame, whereas cl can but must not be specified if data is a list. If specified in the latter case, cl must have length data, i.e.\ one score for each of the matrices, and thus for each of the groups. If not specified, cl will be set to the integers between 1 and c, where c is the number of classes/matrices.

catt

should the Cochran-Armitage trend statistic be computed in the two-class case? If FALSE, the trend statistic described on page 87 of Agresti (2002) is determined which differs by the factor (n - 1) / n from the Cochran-Armitage trend statistic.

approx

should the null distribution be approximated by the Chisquare-distribution with one degree of freedom? If FALSE, a permutation method is used to estimate the null distribution. If data is a list, approx must currently be TRUE.

n.interval

the number of intervals used in the logistic regression with repeated observations for estimating the ratio f0/f (if approx = FALSE), or in the Poisson regression used to estimate the density of the observed z-values (if approx = TRUE). If NULL, n.interval is set to 139 if approx = FALSE, and estimated by the method specified by type.nclass if approx = TRUE.

df.dens

integer specifying the degrees of freedom of the natural cubic spline used in the Poisson regression to estimate the density of the observed z-values. Ignored if approx = FALSE. If NULL, df.dens is set to 3 if the degrees of freedom of the appromimated null distribution, i.e.\ the ChiSquare-distribution, are less than or equal to 2, and otherwise df.dens is set to 5.

knots.mode

if TRUE the df.dens - 1 knots are centered around the mode and not the median of the density when fitting the Poisson regression model. Ignored if approx = FALSE. If not specified, knots.mode is set to TRUE if the degrees of freedom of the approximated null distribution, i.e.\ tht ChiSquare-distribution, are larger than or equal to 3, and otherwise knots.mode is set to FALSE. For details on this density estimation, see denspr.

type.nclass

character string specifying the procedure used to compute the number of cells of the histogram. Ignored if approx = FALSE or n.interval is specified. Can be either "wand" (default), "scott", or "FD". For details, see denspr.

B

the number of permutations used in the estimation of the null distribution, and hence, in the computation of the expected z-values.

B.more

a numeric value. If the number of all possible permutations is smaller than or equal to (1+B.more)*B, full permutation will be done. Otherwise, B permutations are used.

B.max

a numeric value. If the number of all possible permutations is smaller than or equal to B.max, B randomly selected permutations will be used in the computation of the null distribution. Otherwise, B random draws of the group labels are used.

n.subset

a numeric value indicating in how many subsets the B permutations are divided when computing the permuted z-values. Please note that the meaning of n.subset differs between the SAM and the EBAM functions.

fast

if FALSE the exact number of permuted test scores that are more extreme than a particular observed test score is computed for each of the variables/SNPs. If TRUE, a crude estimate of this number is used.

df.ratio

integer specifying the degrees of freedom of the natural cubic spline used in the logistic regression with repeated observations. Ignored if approx = TRUE.

rand

numeric value. If specified, i.e. not NA, the random number generator will be set into a reproducible state.

...

ignored.

Value

A list containing statistics required by ebam.

Author(s)

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.

See Also

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

Examples

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## Not run: 
  # 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

## End(Not run)

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