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|
|Bioconductor views||DifferentialExpression GeneExpression Genetics Microarray Sequencing Software StatisticalMethod|
|Maintainer||Anestis Touloumis <[email protected]>|
|Package repository||View on Bioconductor|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.