ArticleProceedings of the National Academy of Sciences of the United States of America2021
Detection of differentially abundant cell subpopulations in scRNA-seq data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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Who cites it
97 citing papers in PubMed, 163 citations in OpenAlex.
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- AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.Nature medicine · 2026Article
- Sharp cell type boundaries emerge from coordinated morphogen signaling.Genes & development · 2026Article
- Dysregulated IL-2 signaling and T-cell subset shifts are associated with eosinophilic esophagitis activity.Biochemistry and biophysics reports · 2026Article
- Single-Cell RNA-seq Analysis Reveals Distinct Tumor and Immunosuppressive T-Cell Phenotypes in Patients with CLL Treated with Ibrutinib.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Single-cell analysis of the human immune system reveals sex-specific dynamics of immunosenescence.Nature aging · 2026Article
- Tumor-infiltrating immature innate lymphoid cells in colorectal cancer are biased toward ILC1/tissue-resident NK cell differentiation.Nature communications · 2026Article
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- Single-cell profiling of tumor lineage plasticity and the immune microenvironment in transformed small cell lung cancer.Journal of translational medicine · 2026Article
- Single Cell Transcriptomics and Surface Protein Expression Reveal Distinct Cellular and Molecular Phenotypes in Human RPESC-RPE and PSC-RPE.bioRxiv : the preprint server for biology · 2026Article
- Single-cell RNA sequencing data analysis reveals differential expressions of ion channels in immunity response to pulmonary COVID-19 infection.Biochemical and biophysical research communications · 2026Article
- Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity.World journal of clinical oncology · 2026Review
- Exploring phenotype-related single-cells through attention-enhanced representation learning.Genome medicine · 2026Article
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
- Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.Genome medicine · 2026Article
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- Assessing differential cell composition in single-cell studies using voomCLR.Bioinformatics (Oxford, England) · 2026Article
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Interpretable Differential Abundance Signature (iDAS).Small methods · 2026Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its
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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.