ArticleBMC bioinformatics2016
CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Estimating Genome-Wide Phylogenies Using Probabilistic Topic Modeling.Systematic biology · 2025Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.Nature methods · 2025Article
- I-SVVS: integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data.Briefings in bioinformatics · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Detecting global and local hierarchical structures in cell-cell communication using CrossChat.Nature communications · 2024Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.Scientific reports · 2024Article
- scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.Briefings in bioinformatics · 2024Article
- Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.Future oncology (London, England) · 2024Review
- scCURE identifies cell types responding to immunotherapy and enables outcome prediction.Cell reports methods · 2023Article
- A new and effective two-step clustering approach for single cell RNA sequencing data.BMC genomics · 2023Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.Genome biology · 2023Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.bioRxiv : the preprint server for biology · 2023Article
- scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.Cancers · 2023Article
- An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation.Briefings in bioinformatics · 2023Article
- SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.Biomolecules · 2023Article
- Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes.Journal of personalized medicine · 2023Article
- Single-cell RNA sequencing reveals cancer stem-like cells and dynamics in tumor microenvironment during cholangiocarcinoma progression.Frontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths.
resultsHere we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights.
conclusionsWith cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .
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