SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell technologies generate massive gene expression datasets, necessitating advanced analytical tools.
• Interpreting complex biological processes relies on gene co-expression network analysis.
• High-dimensional Gaussian graphical models (GGMs) offer rigorous statistical inference for gene-gene dependencies.

Purpose of the Study:

• To introduce SILGGM, an R package for statistical inference in large-scale GGMs.
• To provide efficient and statistically sound methods for analyzing gene co-expression networks.
• To enable reliable identification of gene networks from high-dimensional single-cell data.

Main Methods:

• Development of the SILGGM R package implementing four statistical inference approaches for high-dimensional GGMs.
• Incorporation of a novel and consistent false discovery rate (FDR) procedure.
• Design for user-friendly output compatible with interactive network visualization platforms.

Main Results:

• SILGGM demonstrates significant acceleration compared to existing MATLAB and R implementations.
• The package's methods are validated for large-scale settings with up to ten thousand genes.
• Application to single-cell RNA-seq data reveals biologically meaningful gene networks outperforming conventional methods.

Conclusions:

• SILGGM provides a powerful and efficient tool for statistical inference in large-scale gene co-expression networks.
• The package enhances the analysis of complex biological processes using single-cell data.
• SILGGM facilitates the discovery of reliable gene networks with improved biological relevance.