Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables.
|Author||Anestis Touloumis, John C. Marioni and Simon Tavare|
|Date of publication||None|
|Maintainer||Anestis Touloumis <A.Touloumis@brighton.ac.uk>|
centerdata: Centering Transposable Data
covmat.hat: Estimation of the Row and of the Column Covariance Matrices.
covmat.ts: Nonparametric Tests for the Row or Column Covariance Matrix
HDTD: Estimation and Hypothesis Testing in High-Dimensional...
meanmat.hat: Estimation the Mean Matrix
meanmat.ts: Nonparametric Tests for the Mean Matrix
orderdata: Reordering Row and Column Variables
transposedata: Interchanging the Row and Column Variables in Transposable...
VEGFmouse: Vascular Endothelial Growth Factor Mouse Dataset