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

Bioconductor version: Release (3.19)

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 [cre, aut] , John C. Marioni [aut] , Simon Tavar\'{e} [aut]

Maintainer: Anestis Touloumis <A.Touloumis at>

Citation (from within R, enter citation("HDTD")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


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

HDTD to Analyze High-Dimensional Transposable Data HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GeneExpression, Genetics, Microarray, Sequencing, Software, StatisticalMethod
Version 1.38.0
In Bioconductor since BioC 3.0 (R-3.1) (10 years)
License GPL-3
Depends R (>= 4.1)
Imports stats, Rcpp (>= 1.0.1)
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Source Package HDTD_1.38.0.tar.gz
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macOS Binary (x86_64) HDTD_1.38.0.tgz
macOS Binary (arm64) HDTD_1.38.0.tgz
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