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

🤖 Plain-English 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. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules.

🔑 Key Findings

• 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.

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