GlobTestMissing: Detection of global group effect

In RepeatedHighDim: Methods for High-Dimensional Repeated Measures Data

Detection of global group effect

Description

Detection of global group effect

Usage

GlobTestMissing(X1, X2, nperm = 100)

Arguments

X1	Matrix of expression levels in first group. Rows represent features, columns represent samples.
X2	Matrix of expression levels in second group. Rows represent features, columns represent samples.
nperm	Number of permutations.

Details

Tests a global effect for a set of molecular features (e.g. genes, proteins,...) between the two groups of samples. Missing values are allowd in the expression data. Samples of the two groups are supposed to be unpaired.

Value

The p-value of a permutation test.

Author(s)

Klaus Jung

References

Jung K, Dihazi H, Bibi A, Dihazi GH and Beissbarth T (2014): Adaption of the Global Test Idea to Proteomics Data with Missing Values. Bioinformatics, 30, 1424-30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btu062")}

See Also

For more information, please refer to the package's documentation and the tutorial: https://software.klausjung-lab.de/.

Examples

### Global comparison of a set of 100 proteins between two experimental groups,
### where (tau * 100) percent of expression levels are missing.
n1 = 10
n2 = 10
d = 100
tau = 0.1
X1 = t(matrix(rnorm(n1*d, 0, 1), n1, d))
X2 = t(matrix(rnorm(n2*d, 0.1, 1), n2, d))
X1[sample(1:(n1*d), tau * (n1*d))] = NA
X2[sample(1:(n2*d), tau * (n2*d))] = NA
GlobTestMissing(X1, X2, nperm=100)

RepeatedHighDim documentation built on April 15, 2025, 1:28 a.m.