ArticleBriefings in bioinformatics2021
Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Spatial dynamics of viral replication drive biphasic lymphoid activation and systemic alphavirus immune responses.PLoS pathogens · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Deciphering the Heterogeneity of Pulmonary Macrophages in Response to Fine Particles.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Cellular interactions within the immune microenvironment underpins resistance to cell cycle inhibition in breast cancers.Nature communications · 2025Article
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.Cell reports methods · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.bioRxiv : the preprint server for biology · 2024Article
- Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.Cell reports methods · 2024Article
- Advancements in technology for characterizing the tumor immune microenvironment.International journal of biological sciences · 2024Review
- MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.Briefings in bioinformatics · 2023Article
- Review
- sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.Frontiers in immunology · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.
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.