ArticleNAR genomics and bioinformatics2023
scROSHI: robust supervised hierarchical identification of single cells.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A tumor profiling resource for ovarian cancer: insights into chemotherapy-driven heterogeneity and personalized treatment strategy.Nature communications · 2026Article
- Ex vivo drug sensitivity testing predicts treatment outcomes in advanced ovarian cancer.NPJ precision oncology · 2026Article
- Ovarian cancer metastasis to the human omentum disrupts organ homeostasis and induces fundamental tissue reprogramming.Nature communications · 2025Article
- Serine/threonine kinase 33 mediates thrombin-induced interleukin-8 release from human lung epithelial cells in severe asthma.Respiratory research · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia.Nature communications · 2024Article
- Uncertainty-aware single-cell annotation with a hierarchical reject option.Bioinformatics (Oxford, England) · 2024Article
- Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer.Nature communications · 2023Article
- Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma.Communications biology · 2023Article
- scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.
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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.