https://github.com/anestistouloumis/hdtd
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.
https://github.com/anestistouloumis/hdtd
bioconductor-package high-dimensional statistics
Last synced: about 1 year ago
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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.
- Host: GitHub
- URL: https://github.com/anestistouloumis/hdtd
- Owner: AnestisTouloumis
- Created: 2017-08-23T13:19:12.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2021-07-30T17:24:01.000Z (almost 5 years ago)
- Last Synced: 2025-05-07T20:17:10.299Z (about 1 year ago)
- Topics: bioconductor-package, high-dimensional, statistics
- Language: C++
- Homepage: http://bioconductor.org/packages/HDTD/
- Size: 4.18 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
references:
- id: Touloumis2016
title: "HDTD: Analyzing Multi-tissue Gene Expression Data"
author:
- family: Touloumis
given: Anestis
- family: Marioni
given: John C.
- family: Tavaré
given: Simon
container-title: Bioinformatics
volume: 32
URL: 'https://doi.org/10.1093/bioinformatics/btw224'
issue: 14
page: 2193--2195
type: article-journal
issued:
year: 2016
- id: Touloumis2015
title: "Testing the Mean Matrix in High-Dimensional Transposable Data"
author:
- family: Touloumis
given: Anestis
- family: Tavaré
given: Simon
- family: Marioni
given: John C.
container-title: Biometrics
volume: 71
URL: 'http://onlinelibrary.wiley.com/doi/10.1111/biom.12054/full'
issue: 1
page: 157--166
type: article-journal
issued:
year: 2015
- id: Touloumis2013
title: "Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure"
author:
- family: Touloumis
given: Anestis
- family: Marioni
given: John C.
- family: Tavaré
given: Simon
container-title: Statistica Sinica
volume: 31
URL: 'http://www3.stat.sinica.edu.tw/statistica/J31N3/J31N309/J31N309.html'
issue: 3
page: 1309--1329
type: article-journal
issued:
year: 2021
csl: Biometrics.csl
---
```{r, include = FALSE}
knitr::opts_chunk$set(
tidy = TRUE,
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# HDTD: Analyzing High-Dimensional Transposable Data
[](https://travis-ci.org/AnestisTouloumis/HDTD)
[](http://www.repostatus.org/#active)
## Installation
You can install the release version of `HDTD`:
```{r eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HDTD")
```
The source code for the release version of `HDTD` is available on Bioconductor at:
- http://bioconductor.org/packages/release/bioc/html/HDTD.html
Or you can install the development version of `HDTD`:
```{r eval=FALSE}
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("AnestisTouloumis/HDTD")
```
To use `HDTD`, you should load the package as follows:
```{r}
library("HDTD")
```
## Usage
This package offers functions to estimate and test the matrix parameters of transposable data in high-dimensional settings. The term _transposable data_ refers to datasets that are structured in a matrix form such that both the rows and columns correspond to variables of interest and dependencies are expected to occur _among_ rows, _among_ columns and _between_ rows and columns. For example, consider microarray studies in genetics where multiple RNA samples across different tissues are available per subject. In this case, a data matrix can be created with row variables the genes, column variables the tissues and measurements the corresponding expression levels. We expect dependencies to occur among genes, among tissues and between genes and tissues. For more examples of transposable data see references in @Touloumis2013, @Touloumis2015 and @Touloumis2016.
There are four core functions:
- `meanmat.hat` to estimate the mean matrix of the transposable data,
- `meanmat.ts` to test the overall mean of the row (column) variables across groups of column (row) variables,
- `covmat.hat` to estimate the row and column covariance matrix,
- `covmat.ts` to test the sphericity, identity and diagonality hypothesis test for the row/column covariance matrix.
There are also three utility functions:
- `transposedata` for interchanging the role of rows and columns,
- `centerdata` for centering the transposable data around their mean matrix,
- `orderdata` for rearranging the order of the row and/or column variables.
## Example
We replicate the analysis that can be found in the vignette based on the mouse dataset
```{r}
data(VEGFmouse)
```
This dataset contains expression levels for $40$ mice. For each mouse, the expression levels of $46$ genes (rows) that belong to the vascular endothelial growth factor signalling pathway were measured across $9$ tissues (adrenal gland, cerebrum, hippocampus, kidney, lung, muscle, spinal cord, spleen and thymus) that are displayed in the columns.
One can estimate the mean relationship of the gene expression levels across the $9$ tissues
```{r}
sample_mean <- meanmat.hat(datamat = VEGFmouse,N=40)
sample_mean
```
and test whether the overall gene expression is constant across the $9$ tissues:
```{r}
tissue_mean_test <- meanmat.ts(datamat = VEGFmouse,N=40,group.sizes=9)
tissue_mean_test
```
In this case, the overall gene expression is not conserved.
To analyze the gene-wise and tissue-wise dependence structure, one needs to estimate the two covariance matrices:
```{r}
est_cov_mat <- covmat.hat(datamat=VEGFmouse,N=40)
est_cov_mat
```
Finally, the package allows users to perform hypothesis tests for the covariance matrix of the genes
```{r}
genes_cov_test <- covmat.ts(VEGFmouse,N=40)
genes_cov_test
```
and of the tissues:
```{r}
tissues_cov_test <- covmat.ts(VEGFmouse,N=40,voi="columns")
tissues_cov_test
```
At a $5\%$ significance level, it appears that the genes are correlated but we do not have enough evidence to reject the hypothesis that the tissues are uncorrelated.
## Getting help
The statistical methods implemented in `HDTD` are described in @Touloumis2013, @Touloumis2015 and @Touloumis2016. Detailed examples of `HDTD` can be found in @Touloumis2016 or in the vignette:
```{r, eval=FALSE}
browseVignettes("HDTD")
```
## How to cite
```{r, echo=FALSE, comment=""}
print(citation("HDTD"), bibtex = TRUE)
```
# References