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

View source: R/GlobalAncova.decomp.R

Computation of a F-test for the association between expression values and clinical entities. The test is carried out by comparison of corresponding linear models via the extra sum of squares principle. In models with various influencing factors extra sums of squares can be treated with sequential and type III decomposition. Adjustment for global covariates, e.g. gene expression values in normal tissue as compared to tumour tissue, can be applied. Given theoretical p-values may not be appropriate due to correlations and non-normality. The functions are hence seen more as a descriptive tool.

1 |

`xx` |
Matrix of gene expression data, where columns correspond to samples
and rows to genes. The data should be properly normalized beforehand
(and log- or otherwise transformed). Missing values are not allowed.
Gene and sample names can be included as the row and column
names of |

`formula` |
Model formula for the linear model. |

`model.dat` |
Data frame that contains all the variable information for each sample. |

`method` |
Whether sequential or type III decomposition or both should be calculated. |

`test.genes` |
Vector of gene names or a list where each element is a vector of gene names. |

`genewise` |
Shall the sequential decomposition be displayed for each single gene in a (small) gene set? |

`zz` |
Global covariate, i.e. matrix of same dimensions as |

`zz.per.gene` |
If set to |

Depending on parameters `test.genes`

, `method`

and `genewise`

ANOVA tables, or lists of ANOVA tables for each
decomposition and/or gene set, or lists with components of ANOVA tables for each gene are returned.

This work was supported by the NGFN project 01 GR 0459, BMBF, Germany.

Ramona Scheufele [email protected]

Reinhard Meister [email protected]

Manuela Hummel [email protected]

Urlich Mansmann [email protected]

`Plot.sequential`

, `pair.compare`

, `GlobalAncova`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(vantVeer)
data(phenodata)
data(pathways)
# sequential or type III decomposition
GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "sequential", test.genes = pathways[1:3])
GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "type3", test.genes = pathways[1:3])
# adjustment for global covariate
data(colon.tumour)
data(colon.normal)
data(colon.pheno)
GlobalAncova.decomp(xx = colon.tumour, formula = ~ UICC.stage + sex + location, model.dat = colon.pheno, method = "all", zz = colon.normal)
``` |

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