colTDTsam(mat.snp, model = c("additive", "dominant", "recessive", "max"), approx = NULL, B = 1000, size = 10, chunk = 100, rand = NA)
colTDTebam(mat.snp, model = c("additive", "dominant", "recessive", "max"), approx = NULL, B = 1000, size = 10, chunk = 100, n.interval = NULL, df.ratio = 3, df.dens = 3, knots.mode = TRUE, type.nclass = c("wand", "FD", "scott"), fast = FALSE, rand = NA)mat.snp must consist of the genotypes of father, mother, and offspring
(in this order), where the genotypes must be coded by 0, 1, and 2. Missing values are allowed and need to be coded by NA.
This matrix might be generated from a data frame in ped format by, e.g., employing ped2geno.
"additive" (default), "dominant",
"recessive", or "max". If model = "max", the maximum over the gTDT statistics for testing an additive, dominant,
and recessive model is used as gTDT statistic. Abbreviations are allowed. Thus, e.g., model = "dom" will
fit a dominant model, and model = "r" an recessive model.
approx = FALSE, the null distribution is estimated based on a permutation method.
If not specified, i.e. NULL, approx is set to TRUE, when an additive, dominant,
or recessive mode of inheritance is considered, and approx = FALSE, when model = "max".
If model = "max", it is not allowed to set approx = TRUE.
approx = TRUE.
approx = FALSE), or in the Poisson regression used to estimate
the density of the observed gTDT values (if approx = TRUE). For details, see Efron et al., 2001,
or Schwender and Ickstadt, 2008, respectively.
If NULL, n.interval is determined by the maximum of 139 (see Efron et al., 2001) and the
number of intervals estimated by the method specified by type.nclass.
approx is
set to FALSE.
approx is set to TRUE.
df.dens - 1 knots of the natural cubic spline are centered around the
mode and not the median of the density when fitting the Poisson regression model to estimate
the density of the observed gTDT values in an EBAM analysis. Only used when approx is set to TRUE.
For details on this density estimation, see denspr.
n.interval). Can be either "wand" (default), "FD", or "scott".
Ignored if n.interval is specified. For details, see denspr.
FALSE, the exact number of permuted test scores larger than the respective
observed gTDT value is computed.
NA, the random number generator
will be set into a reproducible state.
colTDTsam or colTDTebam is an object of class SAM or EBAM, respectively. All the
features implemented in the R package siggenes for an SAM or EBAM analysis, respectively, can therefore be
used in the SAM or EBAM analysis of case-parent trio data implemented in colTDTsam or colTDTebam, respectively.
For details, see sam or ebam, respectively.
Schwender, H. and Ickstadt, K. (2008). Empirical Bayes Analysis of Single Nucleotide Polymorphisms. BMC Bioinformatics, 9, 144.
Schwender, H., Taub, M.A., Beaty, T.H., Marazita, M.L., and Ruczinski, I. (2011). Rapid Testing of SNPs and Gene-Environment Interactions in Case-Parent Trio Data Based on Exact Analytic Parameter Estimation. Biometrics, 68, 766-773.
Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. Proceedings of the National Academy of Science of the United States of America, 98, 5116-5121.
colTDT, colTDTmaxStat, sam, ebam,
SAM-class, EBAM-class
# Load the simulated data.
data(trio.data)
# Perform a Significance Analysis of Microarrays (SAM).
sam.out <- colTDTsam(mat.test)
# By default an additive mode of inheritance is considered.
# If another mode, e.g., the dominant mode, should be
# considered, then this can be done by
samDom.out <- colTDTsam(mat.test, model="dominant")
# Analogously, an Empirical Bayes Analysis of Microarrays based
# on the genotypic TDT can be performed by
ebam.out <- colTDTebam(mat.test)
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