Evidence map›Paper›PMID 37924118›Full record

ArticleGenome medicine2023

Annotation of cell types (ACT): a convenient web server for cell type annotation.

Fei Quan, Xin Liang, Mingjiang Cheng, Huan Yang, Kun Liu, Shengyuan He, Shangqin Sun, Menglan Deng, Yanzhen He, Wei Liu and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
10.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 69 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 1 institution in 1 country.

Fei Quan *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Xin Liang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Mingjiang Cheng *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Huan YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Kun LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Shengyuan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Shangqin SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Menglan DengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Yanzhen HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Wei LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Shuai WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Shuxiang ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Lantian DengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Xiaobo HouCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Xinxin Zhang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. zhangxinxin@hrbmu.edu.cn.
Yun Xiao *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. xiaoyun@ems.hrbmu.edu.cn.ORCID 0000-0003-0785-3957
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge.

methodsTo address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods.

resultsBy integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified.

conclusionsWe developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.

Indexed as

InternetSoftwareHumansMolecular Sequence AnnotationSequence AnalysisCell type annotationCell type enrichment methodEasy-to-use web serverHierarchically organized marker mapSingle-cell RNA sequencing

Identifiers

PMID37924118
PMCPMC10623726
OpenAlexW4388292371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.