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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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GeneNetTools: tests for Gaussian graphical models with shrinkage.

Victor Bernal^1,2, Venustiano Soancatl-Aguilar¹, Jonas Bulthuis¹

¹Center of Information Technology, University of Groningen, Groningen 9747 AJ, The Netherlands.

Bioinformatics (Oxford, England)

|September 30, 2022

Summary

This summary is machine-generated.

Accurate gene network analysis requires accounting for variable number, sample size, and shrinkage in Gaussian graphical models. We developed new statistical methods and an R package to address these limitations for improved biological interpretation.

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Area of Science:

Statistics
Bioinformatics
Computational Biology

Background:

Gaussian graphical models (GGMs) are used for gene regulatory network reconstruction from gene-expression data.
Shrinkage methodologies are applied in GGMs to handle high-dimensional data.
Existing methods often ignore that partial correlations are shrunk, leading to biased differential network analyses.

Purpose of the Study:

To derive statistical properties of partial correlations obtained with Ledoit-Wolf shrinkage.
To develop a toolbox for differential network analyses that accounts for key factors.
To provide accurate and computationally efficient methods for network analysis.

Main Methods:

Derivation of statistical properties for Ledoit-Wolf shrunk partial correlations.
Development of confidence intervals and tests for zero and comparative partial correlations.
Parametric methods accounting for number of variables, sample size, and shrinkage value.

Main Results:

Novel statistical methods for (differential) network analysis were derived.
The developed methods demonstrated better performance than DiffNetFDR in simulations.
The methods were successfully applied to synthetic and real gene-expression datasets.

Conclusions:

Accurate differential network analysis necessitates accounting for shrinkage, variable number, and sample size.
The novel methods provide a statistically sound and computationally efficient approach.
The R package 'GeneNetTools' facilitates the implementation of these methods.