Evidence map›Paper›PMID 41168710›Full record

ArticleBMC genomics2025

Integrating single-cell RNA-seq datasets with substantial batch effects.

Karin Hrovatin, Amir Ali Moinfar, Luke Zappia, Shrey Parikh, Alejandro Tejada Lapuerta, Ben Lengerich, Manolis Kellis, Fabian J Theis

Abstract read
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Review
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  3. An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid.bioRxiv : the preprint server for biology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Karin Hrovatin *Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.ORCID http://orcid.org/0000-0003-3319-9645
Amir Ali Moinfar *Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.ORCID http://orcid.org/0009-0005-4680-2724
Luke ZappiaInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.ORCID http://orcid.org/0000-0001-7744-8565
Shrey ParikhInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.ORCID http://orcid.org/0009-0005-0592-1612
Alejandro Tejada LapuertaInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.ORCID http://orcid.org/0000-0001-6213-8820
Ben LengerichComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-8690-9554
Manolis KellisComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-7113-9630
Fabian J TheisInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.ORCID http://orcid.org/0000-0002-2419-1943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integration of single-cell RNA-sequencing (scRNA-seq) datasets is standard in scRNA-seq analysis. Nevertheless, current computational methods struggle to harmonize datasets across systems such as species, organoids and primary tissue, or different scRNA-seq protocols, including single-cell and single-nuclei. Conditional variational autoencoders (cVAE) are a popular integration method, however, existing strategies for stronger batch correction have limitations. Increasing the Kullback-Leibler divergence regularization does not improve integration and adversarial learning removes biological signals. Here, we propose sysVI, a cVAE-based method employing VampPrior and cycle-consistency constraints. We show that sysVI integrates across systems and improves biological signals for downstream interpretation of cell states and conditions.

Indexed as

Computational BiologyRNA-SeqSequence Analysis, RNASingle-Cell AnalysisAlgorithmsAnimalsHumansSingle-Cell Gene Expression AnalysisSoftwareAdversarial learningBenchmarkingData integrationKL regularization strengthLatent cycle-consistencySingle-cell RNA sequencing (scRNA-seq)VampPrior

Identifiers

PMID41168710
PMCPMC12577435

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.