ArticleNature methods2020
Investigating higher-order interactions in single-cell data with scHOT.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems.PLoS computational biology · 2026Article
- Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.Genome research · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Advanced physiological maturation of human iPSC-derived cardiomyocytes using an algorithm-directed optimization of defined media components.Nature communications · 2026Article
- Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.Bioinformatics advances · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Cell-type-specific dysregulation of gene expression due to Chd8 haploinsufficiency during mouse cortical development.Cell genomics · 2025Article
- Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.BMC bioinformatics · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- Cross-expression meta-analysis of 695 brain samples reveals coordinated gene expression across spatially adjacent cells.bioRxiv : the preprint server for biology · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- A computational pipeline for spatial mechano-transcriptomics.Nature methods · 2025Article
- Spatial Transcriptomics in Human Cardiac Tissue.International journal of molecular sciences · 2025Review
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Stability of Ecological Systems: A Theoretical Review.Physics reports · 2024Article
- LIANA+ provides an all-in-one framework for cell-cell communication inference.Nature cell biology · 2024Article
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
- DELVE: feature selection for preserving biological trajectories in single-cell data.Nature communications · 2024Article
- Single-cell RNA sequencing in melanoma: what have we learned so far?EBioMedicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 5 countries.
Funding
Abstract
Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered 'pseudotime' offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT-single-cell higher-order testing-which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.
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Registered trials
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.