Evidence map›Paper›PMID 40829575›Full record

ArticleCell reports methods2025

Anticlustering for sample allocation to minimize batch effects.

Martin Papenberg, Cheng Wang, Maïgane Diop, Syed Hassan Bukhari, Boris Oskotsky, Brittany R Davidson, Kim Chi Vo, Binya Liu, Juan C Irwin, Alexis J Combes and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Martin PapenbergDepartment of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. Electronic address: martin.papenberg@hhu.de.
Cheng WangEli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Maïgane DiopDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
Syed Hassan BukhariBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Boris OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Brittany R DavidsonUCSF CoLabs, University of California, San Francisco, San Francisco, CA, USA.
Kim Chi VoCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Binya LiuCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Juan C IrwinCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Alexis J CombesUCSF CoLabs, University of California, San Francisco, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Pathology, University of California, San Francisco, San Francisco, CA, USA.
Brice GaudilliereDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
Jingjing LiEli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
David K StevensonDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
Gunnar W KlauAlgorithmic Bioinformatics, Institute for Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Center for Digital Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Linda C GiudiceCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. Electronic address: tomiko.oskotsky@ucsf.edu.

Funding

UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI OPOKU-ANANE, JESSICA · 2021 to 2025
$7.1M
NICHD NIH HHS P01 HD106414
6 · The paper itself

Abstract

High-throughput sequencing enables efficient processing of DNA and RNA samples in batches, but batch effects can obscure true biological signal. We propose using anticlustering as an automated method to assign samples to balanced batches, minimizing covariate imbalance and supporting user-defined constraints such as batch size, number of batches, and "must-link" assignments. In simulations, anticlustering outperforms existing methods in assigning balanced batches. We illustrate its utility using a real-life example from the University of California, San Francisco (UCSF)-Stanford Endometriosis Center for Discovery, Innovation, Training and Community Engagement (ENACT) Center, where multiple samples per individual required processing within the same batch to avoid confounding. The Two-Phase Must-Link (2PML) anticlustering algorithm realized the must-link restrictions while balancing disease stage, menstrual cycle phase, case vs. control, and clinical site. All methods are accessible via the free, open-source R package anticlust, with a companion RShiny web app for visualization and interactive batch assignment.

Indexed as

High-Throughput Nucleotide SequencingAlgorithmsEndometriosisFemaleHumansanticlusteringbatch effectsCP: Computational biologyCP: Systems biologyexperimental designhigh-throughput sequencingmust-link constraintssample allocationsample assignment

Identifiers

PMID40829575
PMCPMC12461633

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.