HDTD

Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD)

Bioconductor version: 3.1

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

Maintainer: Anestis Touloumis <Anestis.Touloumis at cruk.cam.ac.uk>

Installation

To install this package, start R and enter:

## try http if https is not available
source("https://bioconductor.org/biocLite.R")
biocLite("HDTD")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("HDTD")

Details

Package Archives

Follow Installation instructions to use this package in your R session.