ArticleGenome biology2024
Scalable identification of lineage-specific gene regulatory networks from metacells with NetID.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Microarray and Single-Cell RNA Sequencing Reveals G-Protein Gene Expression Signatures of Spermatogonia Stem Cell.Stem cell reviews and reports · 2025Article
- KEGNI: knowledge graph enhanced framework for gene regulatory network inference.Genome biology · 2025Article
- Scalable identification of lineage-specific gene regulatory networks from metacells with NetID.Genome biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene-gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.
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