hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Summary

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer’s disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

Graphical abstract

Highlights

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  hdWGCNA constructs co-expression networks in high-dimensional transcriptomics data

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  hdWGCNA provides tools for statistics, visualization, and downstream interpretation

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  hdWGCNA is an open-source R package that uses Seurat data structures

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  hdWGCNA in human diseases demonstrates real-world analysis in complex datasets

Motivation

Single-cell and spatial transcriptomics assays are commonly used to profile the molecular signatures of biological systems, yielding high-dimensional datasets that can be used to model gene regulation across cell types, cell states, and spatial niches. Many statistical tools for high-dimensional transcriptomics data analysis focus on individual features rather than the underlying network structure, ignoring potential interactions between transcripts or genes. Here, we introduce hdWGCNA, a comprehensive methodological framework for the inference, analysis, and interpretation of gene co-expression networks in high-dimensional transcriptomics data. hdWGCNA is implemented as an open-source R package that extends the Seurat ecosystem of data analysis tools.

Abstract

Morabito et al. present hdWGCNA, an open-source R package for gene co-expression network analysis in single-cell and spatial transcriptomics data. hdWGCNA builds networks of genes using correlation information in specific cell subpopulations and spatial domains. Applications of hdWGCNA in autism spectrum disorder and Alzheimer’s disease revealed disease-associated gene networks.