Evidence map›Paper›PMID 39986279›Full record

ArticleCell genomics2025

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

Jiyun Zhou, Chongyuan Luo, Hanqing Liu, Matthew G Heffel, Richard E Straub, Joel E Kleinman, Thomas M Hyde, Joseph R Ecker, Daniel R Weinberger, Shizhong Han

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Jiyun ZhouLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA.
Chongyuan LuoDepartment of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Hanqing LiuGenomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
Matthew G HeffelDepartment of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Richard E StraubLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA.
Joel E KleinmanLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Thomas M HydeLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Joseph R EckerGenomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
Daniel R WeinbergerLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Shizhong HanLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21287, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address: shizhong.han@libd.org.

Funding

Integrative approaches to identification and interpretation of genes underlying psychiatric disordersR01MH121394 · NIMH · LIEBER INSTITUTE, INC. · PI HAN, SHIZHONG · 2020 to 2023
$2.4M
NIMH NIH HHS R01 MH121394
6 · The paper itself

Abstract

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

Indexed as

Deep LearningDNA MethylationEpigenesis, GeneticSchizophreniaSingle-Cell AnalysisAnimalsCpG IslandsHumansMicedeep learningDNA methylationimputationschizophreniatransformer

Identifiers

PMID39986279
PMCPMC11960545

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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.