ArticleMolecular systems biology2020
scClassify: sample size estimation and multiscale classification of cells using single and multiple reference.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 1 of them a synthesis that pooled it.
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Who cites it
103 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- Myeloid reprogramming generates myeloid-derived activating cells to potentiate radiotherapy and suppress metastasis.Cancer cell · 2026Article
- RAGCell: retrieval-augmented generation as supervision for versatile single-cell analysis.Bioinformatics (Oxford, England) · 2026Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- scUmaper: An automated framework for doublet removal and cell-type annotation in single-cell transcriptomics.iScience · 2026Article
- Robust semi-supervised scRNA-seq integration from virtual adversarial learning.bioRxiv : the preprint server for biology · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-cell chromatin accessibility and cis-regulatory element analyses in plants using the scPlantReg platform.Nature plants · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
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- scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.mSystems · 2026Article
- A transposase-derived gene required for human brain development.Science advances · 2026Article
- VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.Communications biology · 2026Article
- scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data.Nucleic acids research · 2026Article
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Automated cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.
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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.