Description Usage Arguments Value Author(s) References See Also Examples

Performs a Significance Analysis of Microarrays (SAM; Tusher et al., 2001) or an Empirical Bayes Analysis of Microarrays (EBAM; Efron et al., 2001), respectively, based on the genotypic transmission/disequilibrium test statistic.

1 2 3 4 5 6 7 | ```
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` |
a matrix in genotype format, i.e. a numeric matrix in which each column is
a vector of length |

`model` |
type of genetic mode of inheritance that should be considered. Either |

`approx` |
logical specifying whether the null distribution should be approximated by a |

`B` |
number of permutations used in the estimation of the null distribution, and thus, the computation of the null statistics.
Ignored if |

`size` |
number of SNPs considered simultaneously when computing the gTDT statistics. |

`chunk` |
number of permutations considered simultaneously in the permutation procedure. |

`n.interval` |
the number of intervals used in the logistic regression with
repeated observations for estimating the ratio of the null density to the density of the observed
gTDT values in an EBAM analysis (if |

`df.ratio` |
integer specifying the degrees of freedom of the natural cubic
spline used in the logistic regression with repeated observations for estimating the ratio of the null
density to the density of the observed gTDT values in an EBAM analysis. Only used when |

`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 gTDT values in an EBAM analysis.
Only used when |

`knots.mode` |
logical specifying whether the |

`type.nclass` |
character string specifying the procedure used to estimate the
number of intervals of the histogram used in the logistic regression with repeated observations or the Poisson regression,
respectively (see |

`fast` |
logical specifying whether a crude estimate for the number of permuted test scores larger than the respective
observed gTDT value should be used. If |

`rand` |
numeric value. If specified, i.e. not |

The output of `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.

Holger Schwender, holger.schwender@udo.edu

Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001).
Empirical Bayes Analysis of a Microarray Experiment, *Journal of the American Statistical Association*,
96, 1151-1160.

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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# 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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