ReviewComputational and structural biotechnology journal2021
Automated methods for cell type annotation on scRNA-seq data.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
147 citing papers in PubMed, 1 synthesis or guideline pooled it, 255 citations in OpenAlex.
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- TORC: Target-Oriented Reference Construction for Supervised Cell-Type Identification in scRNA-seq.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.Interdisciplinary sciences, computational life sciences · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- Application of human induced pluripotent stem cells for tissue modeling and therapy: are we on track?Stem cells (Dayton, Ohio) · 2026Review
- Automatic extraction of mesenchymal stromal cells from single-cell RNA-sequencing data of human dental pulp.BioData mining · 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
- scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations.Journal of advanced research · 2026Article
- Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy.Journal of ovarian research · 2026Review
- Robust semi-supervised scRNA-seq integration from virtual adversarial learning.bioRxiv : the preprint server for biology · 2026Article
- scParadise: tunable, highly accurate multi-level cell type annotation, unknown cell type identification, and modality imputation.Nucleic acids research · 2026Article
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.Bioinformatics (Oxford, England) · 2026Article
- mmContext: an open framework for multimodal contrastive learning of omics and text data.Bioinformatics (Oxford, England) · 2026Article
- Cell-o1 : training LLMs to solve single-cell reasoning puzzles with reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq.PLoS computational biology · 2026Article
- Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution.Molecular neurobiology · 2026Article
87 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
4 authors at 3 institutions in 2 countries.
Funding
Abstract
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on 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.