ArticleCell reports methods2023
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 595 papers, 1 of them a synthesis that pooled it.
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Who cites it
595 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Single-cell multi-omics characterises transcriptional and chromatin accessibility heterogeneity of immune cells in allergic rhinitis.Clinical and translational medicine · 2026Article
- Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics.Molecular psychiatry · 2026Article
- Integrated scRNA-Seq and Spatial Transcriptomics Reveal CHPF and GPC1 as Markers of Cancer-Associated Fibroblast-Dominant Domains in Colorectal Cancer.Cancer medicine · 2026Article
- Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis.Chemical biology & drug design · 2026Article
- Digoxin Alleviates Osteoarthritis by Inhibiting Macrophage Extracellular Trap Formation via Disruption of the LSP1-SOD1 Interaction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Key regulatory elements of the TGFβ-LRRC15 axis predict disease progression and immunotherapy resistance across cancer types.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
535 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.