Evidence map›Paper›PMID 41044605›Full record

ArticleGenome biology2025

Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.

Zeyuan Johnson Chen, Elior Rahmani, Eran Halperin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

3 authors.

Zeyuan Johnson Chen *Department of Computer Science, University of California, Los Angeles, CA, USA.
Elior Rahmani *Department of Computational Medicine, University of California, Los Angeles, CA, USA. eliorrahmani@mednet.ucla.edu.
Eran HalperinDepartment of Computational Medicine, University of California, Los Angeles, CA, USA. ehalperin@cs.ucla.edu.ORCID http://orcid.org/0000-0002-2373-3691

Funding

Methods for Genomic Analysis in Heterogeneous TissuesR01HG010505 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HALPERIN, ERAN · 2019 to 2022
$2.6M
NHGRI NIH HHS 5R01HG010505-04NHGRI NIH HHS R01 HG010505
6 · The paper itself

Abstract

Most population-scale genomic datasets collected to date consist of "bulk" samples obtained from heterogeneous tissues, reflecting mixtures of different cell types. We introduce Unico, a Unified cross-omics computational method designed to deconvolve standard two-dimensional bulk matrices (samples by features) into three-dimensional tensors (samples by features by cell types). Unico is the first principled model-based deconvolution method that is theoretically justified for any tissue-level genomic data. By deconvolving bulk gene expression and DNA methylation datasets, we demonstrate Unico's superior performance compared to existing methods, enhancing the ability to conduct powerful, large-scale genomic studies at cell-type resolution.

Indexed as

GenomicsSoftwareAlgorithmsDNA MethylationHumansCell-type specificityComputational modelsDecompositionDeconvolutionDNA methylationEpigenomicsNonparametric modelsRNA expressionStatistical methods

Identifiers

PMID41044605
PMCPMC12492706

What OpenQuestion holds

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LicenceCC BY
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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.