Evidence map›Paper›PMID 41576958›Full record

ArticleCell reports methods2026

Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.

Dongmei Li, Pinxin Liu, Irfan Rahman, Martin Zand, Gloria Pryhuber, Timothy Dye, Maciej Goniewicz, Aditi Uday Gurkar, Melanie Königshoff, Oliver Eickelberg and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2026. 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. Transcriptomic Analysis Reveals HowLife (Basel, Switzerland) · 2026
    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

18 authors.

Dongmei LiClinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA. Electronic address: dongmei_li@urmc.rochester.edu.
Pinxin LiuDepartment of Computer Science, University of Rochester, Rochester, NY, USA.
Irfan RahmanDepartment of Environmental Medicine, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.
Martin ZandClinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA; Department of Medicine, Division of Nephrology, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.
Gloria PryhuberDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.
Timothy DyeDepartment of Obstetrics and Gynecology, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.
Maciej GoniewiczDepartment of Health Behavior, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
Aditi Uday GurkarAging Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Melanie KönigshoffDivision of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Oliver EickelbergDivision of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Ana MoraDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA.
Mauricio RojasDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA.
Qin MaDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Jose Lugo-MartinezComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Ziv Bar-JosephComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Serafina LannaAging Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Toren FinkelAging Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Zidian XieClinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Differential gene expression (DGE) analysis is a crucial step in identifying senescent cells using single-cell RNA sequencing (scRNA-seq) data. However, few studies have evaluated the performance of DGE methods-particularly those implemented in the widely used Seurat package. In this study, we systematically assessed 10 DGE methods available in Seurat-Wilcox, Wilcox-limma, bimod, roc, t, negbinom, Poisson, LR, MAST, and DESeq2-using simulated and real scRNA-seq datasets. We evaluated each method's performance across varying sample sizes, levels of sparsity, and proportions of truly differentially expressed genes. Metrics assessed included false discovery rate (FDR), sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (AUPRC). Among all methods, DESeq2 consistently demonstrated the best overall performance, showing the highest AUC and AUPRC across all tested conditions. Based on our findings, we recommend DESeq2 as the preferred method for DGE analysis in scRNA-seq data.

Indexed as

Cellular SenescenceGene Expression ProfilingSingle-Cell AnalysisTranscriptomeHumansSingle-Cell Gene Expression AnalysisaccuracyAUCAUPRCCP: computational biologydifferential analysis methodsFDRsenescent cellssensitivitySeuratsingle-cell transcriptomicsspecificity

Identifiers

PMID41576958
PMCPMC12946756

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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.