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Updated: Mar 19, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

HDTD: analyzing multi-tissue gene expression data.

Anestis Touloumis¹, John C Marioni², Simon Tavaré³

¹CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK.

Bioinformatics (Oxford, England)

|June 9, 2016

Summary

This summary is machine-generated.

This study introduces the HDTD R package to analyze complex gene expression data, accounting for dependencies between genes and tissues. It provides robust statistical methods for understanding intra-subject variation in biological samples.

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Intra-subject variation is crucial for understanding biological processes like cell type identity and tumor development.
Gene expression profiling generates complex data where genes and tissues exhibit dependencies.
Traditional statistical methods may yield erroneous conclusions when ignoring these dependencies.

Purpose of the Study:

To present a suite of tools for robustly estimating and testing relationships in multi-sample biological data.
To address the challenge of two-way dependence in gene expression matrices.
To provide a reliable method for analyzing intra-subject variation.

Main Methods:

Development of the R/Bioconductor package HDTD.
Implementation of statistical methods for estimating mean relationships and covariance structures.
Application of the HDTD package to analyze Genotype-Tissue Expression (GTEx) consortium data.

Main Results:

HDTD enables robust estimation of mean relationships within gene expression data.
The package facilitates hypothesis testing for covariance structures, accounting for gene-tissue dependencies.
Demonstrated utility of HDTD in analyzing real-world biological datasets.

Conclusions:

The HDTD package offers a robust solution for analyzing complex, dependent biological data.
Accurate characterization of intra-subject variation is achievable with appropriate statistical tools.
HDTD enhances the understanding of fundamental biological questions using gene expression data.