ArticleFrontiers in cell and developmental biology2019
Cross-Species Analysis of Single-Cell Transcriptomic Data.
Article in Frontiers in cell and developmental biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- The misplaced mouse Pax6 interneuron subclass: A cross-species transcriptomic reassignment.iScience · 2026Article
- A cross-vertebrate single-cell and spatial atlas reveals evolutionary trajectories of gastrointestinal cellular innovation.Cell genomics · 2026Article
- Multi-omic spatial mapping of the human habenula defines the molecular and anatomical organization of medial and lateral subregions.bioRxiv : the preprint server for biology · 2026Article
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- Extreme conservation of cnidarian stinging cell identity despite 600 million years of evolution.bioRxiv : the preprint server for biology · 2026Article
- Cluster replicability in single-cell and single-nucleus atlases of the mouse brain.Nature communications · 2026Article
- Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.Nature communications · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Ortholog transfer stress test: Regulatory edge conservation across species, tissues, and evolutionary distance.PloS one · 2026Article
- A comparative review of single-cell atlases: mapping cellular diversity across species and tissues.Cellular and molecular life sciences : CMLS · 2025Review
- Investigating the evolution and features of regeneration using cnidarians.Integrative and comparative biology · 2025Review
- Review
- UniMap: Type-Level Integration Enhances Biological Preservation and Interpretability in Single-Cell Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.Nucleic acids research · 2025Article
- Single-Cell Profiling Uncovers Evolutionary Divergence of Hypocretin/Orexin Neuronal Subpopulations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.Genome research · 2024Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching.Bioinformatics (Oxford, England) · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to profile hundreds of thousands to millions of single cells using scRNA-sequencing has revolutionized the fields of cell and developmental biology, providing incredible insights into the diversity of forms and functions of cell types across many species. These technologies hold the promise of developing detailed cell type phylogenies which can describe the evolutionary and developmental relationships between cell types across species. This will require sampling of many species and taxa using single-cell transcriptomics, and methods to classify cell type homologies and diversifications. Many tools currently exist for analyzing single cell data and identifying cell types. However, cross-species comparisons are complicated by many biological and technical factors. These factors include batch effects common to deep-sequencing approaches, well known evolutionary relationships between orthologous and paralogous genes, and less well-understood evolutionary forces shaping transcriptome variation between species. In this review, I discuss recent developments in computational methods for the comparison of single-cell-omic data across species. These approaches have the potential to provide invaluable insight into how evolutionary forces act at the level of the cell and will further our understanding of the evolutionary origins of animal and cellular diversity.
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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.