{"id":"doi:10.1016/j.crmeth.2023.100498","kind":"article","title":"hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data","authors":["Morabito S","Reese F","Rahimzadeh N","Miyoshi E","Swarup V"],"author_count":5,"year":2023,"published":"2023-06-12","venue":"Cell reports methods","cited_by":795,"doi":"10.1016/j.crmeth.2023.100498","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.crmeth.2023.100498","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.crmeth.2023.100498","updated":null,"pmid":"37426759","pmcid":"PMC10326379","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10326379/pdf","abstract":"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.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data\" by Morabito S, Reese F, Rahimzadeh N et al., https://doi.org/10.1016/j.crmeth.2023.100498, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"single-cell-spatial","name":"Single-Cell & Spatial Omics","domain_slug":"biotech"}],"cited_in_dossiers":[{"slug":"single-cell-spatial","title":"Single-Cell & Spatial Omics: State of the Field (October 2026)","url":"https://space-kitty.com/dossiers/single-cell-spatial"}]}