ArticleCommunications biology2023
A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- GEfetch2R: fetching single-cell/bulk RNA-seq data from public repositories to R and benchmarking the subsequent format conversion tools.GigaScience · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration.BMC bioinformatics · 2025Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels.Bioinformatics (Oxford, England) · 2024Article
- scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network.Communications biology · 2024Article
- scGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data.Scientific reports · 2024Article
- CHAI: consensus clustering through similarity matrix integration for cell-type identification.Briefings in bioinformatics · 2024Article
- scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.Briefings in bioinformatics · 2024Article
- CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.bioRxiv : the preprint server for biology · 2024Article
- Single-cell Mayo Map (scMayoMap): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis.BMC biology · 2023Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform.
Indexed as
Identifiers
What OpenQuestion holds
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.