Evidence map›Paper›PMID 41915012›Full record

ArticleGigaScience2026

scDenorm: a denormalization tool for integrating single-cell transcriptomics data.

Yin Huang, Anna Vathrakokoili Pournara, Ying Ao, Ziliang Huang, Hui Zhang, Yongjian Zhang, Sheng Liu, Alvis Brazma, Irene Papatheodorou, Xinlu Yang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

12 authors.

Yin HuangTranslational Research Institute of Brain and Brain-Like Intelligence and Department of Anesthesiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.ORCID 0000-0003-1055-2602
Anna Vathrakokoili PournaraEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Cambridge CB10 1SD, UK.ORCID 0000-0002-2701-1987
Ying AoGMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou 511436, China.
Ziliang HuangGuangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China.
Hui ZhangDepartment of Obstetrics and Gynaecology, Harbin Red Cross Central Hospital, Harbin 150001, China.
Yongjian ZhangDepartment of Surgery Oncology, Harbin Medical University Cancer Hospital, Harbin 150001, China.
Sheng LiuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510623, China.ORCID 0000-0002-2497-8666
Alvis BrazmaEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Cambridge CB10 1SD, UK.
Irene PapatheodorouEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Cambridge CB10 1SD, UK.ORCID 0000-0001-7270-5470
Xinlu YangDepartment of Obstetrics and Gynaecology, Harbin Red Cross Central Hospital, Harbin 150001, China.ORCID 0009-0003-7111-3969
Ming ShiSchool of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China.ORCID 0000-0003-0527-0061
Zhichao MiaoGuangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China.ORCID 0000-0002-5777-9815

Funding

Major Project of Guangzhou National Laboratory GZNL2023A01006Major Project of Guangzhou National Laboratory GZNL2024A01002National Key Research and Development Program of China 2023YFF1204700National Key Research and Development Program of China 2024YFF1206600National Key Research and Development Program of China 2025YFE0200600Natural Science Foundation of China 32270707R&D Programs of Guangzhou National Laboratory HWYQ23-003R&D Programs of Guangzhou National Laboratory YW-YFYJ0102
6 · The paper itself

Abstract

Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalization methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts delta-method normalized single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorm's performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across 3 datasets.

Indexed as

Computational BiologyGene Expression ProfilingSingle-Cell AnalysisSoftwareTranscriptomeAlgorithmsAnimalsHumansSingle-Cell Gene Expression Analysis

Identifiers

PMID41915012
PMCPMC13142155

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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.