ArticleNature biotechnology2022
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 334 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
334 citing papers in PubMed, 1 synthesis or guideline pooled it, 489 citations in OpenAlex.
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- Biologically-informed integration of drug representations for breast cancer treatment using deep learning.Nature communications · 2025Trial
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- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Cancer-associated fibroblast-derived POSTN as a metabolic "switch" shapes lipid-stressed macrophage polarization in gastric cancer.Redox biology · 2026Article
- Critical role for the TGF-β1/mTORC1 signaling axis in defining the transcriptional identity of CTHRC1Science advances · 2026Article
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- Targeting a Myeloid-Regulatory B Cell Network Reverses Immune Paralysis in Periprosthetic Joint Infections.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CRACD loss drives hypoxia-induced immune escape in gastric cancer.Nature communications · 2026Article
- Identification and functional validation of lipid droplet-associated prognostic biomarkers in breast cancer via integrative multi-omics and machine learning approaches.International journal of clinical oncology · 2026Article
- Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma.Translational oncology · 2026Article
- Identification and functional validation of a four-gene signature associated with radiotherapy resistance in oral squamous cell carcinoma.Translational oncology · 2026Article
- Multi-Omics Integration Identifies a CDH3-Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identifying head and neck squamous cell carcinoma: HPV-associated signature by integrating bulk and single-cell sequencing data.Medicine · 2026Article
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- Integrating single-nucleus RNA sequencing and gene perturbation reveals regulatory networks of intramuscular fat deposition in bovine skeletal muscle.Animal bioscience · 2026Article
- Cerebrovascular vulnerability and fibrosis in human brain aneurysms.Nature neuroscience · 2026Article
- Integrated bioinformatic evaluation unveils a cellular senescence gene signature as a poor prognostic factor in hepatocellular carcinoma.Clinical and experimental medicine · 2026Article
274 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
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
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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.