Plot.sequential

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 <code>xx</code>.

formula

Data frame that contains all the variable information for each sample.

model.dat

Vector of gene names or gene indices specifying a gene set.

test.genes

name.geneset

Plot to show the sum of squares decomposition for each gene into parts according to all variables.


 hplot

We give the following arguments in support of the
GlobalAncova approach: After appropriate normalisation,
gene-expression-data appear rather symmetrical and outliers are
no real problem, so least squares should be rather robust.
ANCOVA with interaction yields saturated data modelling e.g.
different means per group and gene. Covariate adjustment can
help to correct for possible selection bias. Variance
homogeneity and uncorrelated residuals cannot be expected.
Application of ordinary least squares gives unbiased, but no
longer optimal estimates (Gauss-Markov-Aitken). Therefore,
using the classical F-test is inappropriate, due to
correlation. The test statistic however mirrors deviations from
the null hypothesis. In combination with a permutation
approach, empirical significance levels can be approximated.
Alternatively, an approximation yields asymptotic p-values.
This work was supported by the NGFN grant 01 GR 0459, BMBF,
Germany.

Manuela Hummel

GlobalAncova

Calculates a global test for differential gene expression
between groups

Plot.sequential function

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 <code>xx</code>.

Plot to show the sum of squares decomposition for each gene into parts according to all variables.

Plot.sequential: Visualization of sequential decomposition

Description

Usage

Arguments

See Also

Examples