Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

• Published in: BMC bioinformatics 2024-10, Vol.25 (1), p.339-16, Article 339
• Main Authors: Vo, Duong H T, Thorne, Thomas
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Cell differentiation; Cellular structure; Comparative analysis; Computing time; Covariance matrix; Covariance matrix shrinkage; Data analysis; Data mining; Data structures; Dimensional analysis; Evaluation; Gene expression; Gene Expression Profiling - methods; Gene network; Gene regulation; Gene Regulatory Networks; Gene sequencing; Genes; Humans; Metabolic pathways; Network analysis; Normal distribution; Protein interaction; Protein-protein interactions; Proteins; Random variables; Ribonucleic acid; RNA; RNA sequencing; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-cell RNA-seq analysis; Transcriptomics; Workflow
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05946-9

Gene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis. In this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data. The proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein .