RepeatedHighDim-package: Global tests for expression data of high-dimensional sets of...

In jkruppa/RepeatedHighDim: Global tests for expression data of high-dimensional sets of molecular features.

Description

Global tests for expression data of high-dimensional sets of molecular features. Global tests for expression data of high-dimensional sets of molecular features.

References

• Brunner, E (2009) Repeated measures under non-sphericity. Proceedings of the 6th St. Petersburg Workshop on Simulation, 605-609.

• Jung K, Becker B, Brunner B and Beissbarth T (2011) Comparison of Global Tests for Functional Gene Sets in Two-Group Designs and Selection of Potentially Effect-causing Genes. Bioinformatics, 27, 1377-1383.

• Jung K, Dihazi H, Bibi A, Dihazi GH and Bei\ssbarth T (2014) Adaption of the Global Test Idea to Proteomics Data with Missing Values. Bioinformatics, 30, 1424-30.

• Kruppa J, Kramer K, Bei\ssbarth T and Jung K (2016) A simulation framework for correlated count data of feature subsets in high-throughput sequencing or proteomics experiments. Statistical Applications in Genetics and Molecular Biology, 15, 401-414

Examples

### Global comparison of a set of 100 genes between two experimental groups.
X1 = matrix(rnorm(1000, 0, 1), 10, 100)
X2 = matrix(rnorm(1000, 0.1, 1), 10, 100)
RHD = RepeatedHighDim(X1, X2, paired=FALSE)
summary(RHD)

### 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)

jkruppa/RepeatedHighDim documentation built on May 19, 2019, 12:45 p.m.