Evidence map›Paper›PMID 41411311›Full record

ArticlePloS one2025

Single-cell RNA-seq data normalization: A benchmarking study.

Qinyu Ge, Yuqi Sheng, Junru Lu, Yuwei Yang, Min Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Qinyu GeState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.ORCID https://orcid.org/0000-0002-1708-1949
Yuqi ShengState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Junru LuState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Yuwei YangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Min PanSchool of Medicine, Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the noise and biases introduced by technical factors in single-cell RNA sequencing (scRNA-seq) data, presenting a thorough benchmarking analysis of six widely utilized normalization methods. The evaluation of these methods is conducted from three perspectives: cell clustering, differential expression analysis, and computational resource requirements, utilizing seven real datasets alongside four simulated datasets. The findings indicate that Dino excels in clustering 10 × datasets and those with a substantial number of cells, while scTransform demonstrates strong performance with datasets produced through full-length library preparation protocols. Additionally, SCnorm is identified as suitable for small-scale datasets. This research serves as a significant reference for scholars in selecting appropriate normalization tools, thereby enhancing the accuracy and reliability of subsequent analyses of scRNA-seq data.

Indexed as

RNA-SeqSequence Analysis, RNASingle-Cell AnalysisBenchmarkingCluster AnalysisGene Expression ProfilingHumansSingle-Cell Gene Expression AnalysisSoftware

Identifiers

PMID41411311
PMCPMC12714245

What OpenQuestion holds

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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.