Evidence map›Paper›PMID 39402623›Full record

ArticleGenome biology2024

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq.

Luxiao Chen, Zhenxing Guo, Tao Deng, Hao Wu

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Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

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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

4 citing papers in PubMed.

  1. Review
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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

4 authors.

Luxiao Chen *Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.
Zhenxing Guo *School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), Shenzhen, 518172, Guangdong, China.
Tao DengSchool of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), Shenzhen, 518172, Guangdong, China.
Hao WuFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518055, Guangdong, China. wuhao@siat.ac.cn.ORCID http://orcid.org/0000-0003-1269-7354

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Indexed as

RNA-SeqSingle-Cell AnalysisAnimalsGene Expression ProfilingGenetic MarkersHumansModels, StatisticalSequence Analysis, RNASingle-Cell Gene Expression AnalysisTranscriptomeGenetic MarkersCell type-specific genesDifferential expressionHierarchical modelSingle-cell RNA-seq

Identifiers

PMID39402623
PMCPMC11472465

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Registered trials

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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.