ArticleNucleic acids research2021
Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Rare coding variation provides insight into the genetic architecture and phenotypic context of autism.Nature genetics · 2022Pooled it
- Traumatic brain injury induces cell-type-specific remodelling of the proteostasis network.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- scMustree: a multi-scale functional hierarchy for single-cell transcriptomic analysis.Bioinformatics (Oxford, England) · 2026Article
- Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus.bioRxiv : the preprint server for biology · 2026Article
- Pan-Angiosperm Analysis of the CLE Signaling Peptide Gene Family Unveils Paths, Patterns, and Predictions of Paralog Diversification.Molecular biology and evolution · 2025Article
- Melanocytes and photosensory organs share a common ancestry that illuminates the origins of the neural crest.Communications biology · 2025Article
- CeiTEA: Adaptive Hierarchy of Single Cells with Topological Entropy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Neuronal activity modulates the expression of secretagogin, a CaActa physiologica (Oxford, England) · 2025Article
- Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- DeepDoublet identifies neighboring cell-dependent gene expression.Genomics & informatics · 2024Article
- ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION.The annals of applied statistics · 2024Article
- Transient expression of the neuropeptide galanin modulates peripheral‑to‑central connectivity in the somatosensory thalamus during whisker development in mice.Nature communications · 2024Article
- Molecularly stratified hypothalamic astrocytes are cellular foci for obesity.Research square · 2024Article
- Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus.Science advances · 2023Article
- Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.Briefings in bioinformatics · 2023Article
- scAce: an adaptive embedding and clustering method for single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- Identification of a unique subset of tissue-resident memory CD4Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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6 authors.
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Abstract
A wealth of clustering algorithms are available for single-cell RNA sequencing (scRNA-seq) data to enable the identification of functionally distinct subpopulations that each possess a different pattern of gene expression activity. Implementation of these methods requires a choice of resolution parameter to determine the number of clusters, and critical judgment from the researchers is required to determine the desired resolution. This supervised process takes significant time and effort. Moreover, it can be difficult to compare and characterize the evolution of cell clusters from results obtained at one single resolution. To overcome these challenges, we built Multi-resolution Reconciled Tree (MRtree), a highly flexible tree-construction algorithm that generates a cluster hierarchy from flat clustering results attained for a range of resolutions. Because MRtree can be coupled with most scRNA-seq clustering algorithms, it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. The constructed trees from multiple scRNA-seq datasets effectively reflect the extent of transcriptional distinctions among cell groups and align well with levels of functional specializations among cells. Importantly, application to fetal brain cells identified subtypes of cells determined mainly by maturation states, spatial location and terminal specification.
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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.