ArticleNucleic acids research2019
cellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-species meta-analysis identifies transcriptional signatures associated with cardiac endothelial responses in the ischaemic heart.Cardiovascular research · 2023Pooled it
- Dysregulated NF-κB dynamics impair proteostasis and drive premature differentiation in aging B cell development.Cell press blue · 2026Article
- BLIMP1 shapes germinal center B cell clonal diversity by gating chromatin accessibility during light-to-dark zone transition.Nature immunology · 2026Article
- Metallothionein 1 mediates growth and survival ofHaematologica · 2026Article
- Diverse Transcriptional Alterations in V2a Propriospinal Neurons Following Spinal Cord Injury.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A unified multimodal single-cell framework reveals a discrete state model of hematopoiesis in mice.Nature immunology · 2025Article
- Evidence for Transcriptomic Conservation Between the Main Cells of thebioRxiv : the preprint server for biology · 2025Article
- CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.Cell genomics · 2025Article
- Molecular mechanisms promoting long-term cytopenia after BCMA CAR-T therapy in multiple myeloma.Blood advances · 2024Article
- Review
- Temporal dynamics and genomic programming of plasma cell fates.Nature immunology · 2024Article
- An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors.Nature immunology · 2024Article
- Article
- Population-level single-cell genomics reveals conserved gene programs in systemic juvenile idiopathic arthritis.The Journal of clinical investigation · 2023Article
- Sinus venosus adaptation models prolonged cardiovascular disease and reveals insights into evolutionary transitions of the vertebrate heart.Nature communications · 2023Article
- ThPOK is a critical multifaceted regulator of myeloid lineage development.Nature immunology · 2023Article
- Guided construction of single cell reference for human and mouse lung.Nature communications · 2023Article
- pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells.Bioinformatics (Oxford, England) · 2023Article
- BAFF and APRIL counterregulate susceptibility to inflammation-induced preterm birth.Cell reports · 2023Article
- Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data.Biomolecules · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify distinct and shared gene programs among cell-types and identify impacted pathways and transcriptional regulatory networks to understand the impact of perturbations at a systems level. cellHarmony is implemented as a python package and as an integrated workflow within the software AltAnalyze. We demonstrate that cellHarmony has improved or equivalent performance to alternative label projection methods, is able to identify the likely cellular origins of malignant states, stratify patients into clinical disease subtypes from identified gene programs, resolve discrete disease networks impacting specific cell-types, and illuminate therapeutic mechanisms. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex disease.
Indexed as
Identifiers
What OpenQuestion holds
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.